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Estimation of tropical forest biophysical attributes with synergistic use of optical and microwave remote sensing techniques

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ESTIMATION OF TROPICAL FOREST BIOPHYSICAL ATTRIBUTES
WITH SYNERGISTIC USE OF OPTICAL AND MICROWAVE REMOTE
SENSING TECHNIQUES
By
Cuizhen Wang
A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of requirements
for the degree of
DOCTOR OF PHILOSOPHY
Department of Geography
2004
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UMI Number: 3129553
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ABSTRACT
ESTIMATION OF TROPICAL FOREST BIOPHYSICAL ATTRIBUTES
WITH SYNERGISTIC USE OF OPTICAL AND MICROWAVE REMOTE
SENSING TECHNIQUES
By
Cuizhen Wang
Accurate estimates of tropical forest biophysical attributes provide quantitative
information in the assessment of human disturbances and the carbon sequestration in
global climate change studies. The research of my dissertation is thus to develop new
algorithms to estimate tropical forest biophysical parameters (forest fractional cover, leaf
area index, structures, and aboveground biomass) using synergy of optical and radar
remote sensing imagery and radiative transfer models. The case study is in Mae Chaem
Watershed, ChiangMai, Thailand. Groimd data were collected during the field trips
sponsored by research projects.
A linear unmixing model in the vegetation index {MSA VI) domain was built to estimate
forest fractional cover with Landsat ETM+ image. The forest fractional cover map was
validated using both ground measurements and high-resolution IKONOS images. The
estimated fractional cover correlated with the ground-measured fractional cover
(R^=0.76) at 32 study sites and correlated with the IKONOS-calculated fractional cover
(R^=0.70) at 400 randomly selected locations. The leaf area index was estimated using a
modified Gaussian regression model with forest fractional cover results. The model was
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examined with a
goodness-of-fit test. The correlation coefficient between the modeled
and ground-measured leaf area index values is 0.90.
A microwave/optical synergistic radiative transfer model was built to simulate the radar
scattering from the forest components. The leaf scattering and its attenuation to the
woody components (branches, trunks) were quantified with the leaf area index derived
from optical remote sensing data. The forest structural parameters, such as tree height and
stand density, were estimated through model inversion with JERS-1 SAR and VNIR data.
The total root-mean-square error (RMSE) of tree height estimation was 4.6 meter and that
of stand density estimation was 300 trees/ha. The stand density estimation did not work
in tropical evergreen forests because of its saturation at around 500 trees/ha. In
accordance with ground measurements, tree height is negatively correlated with stand
density in the study area. The model inversion becomes questionable at mountainous
areas with high relief and steep slopes. The aboveground forest biomass is also calculated
with allometric equations and the modeled forest structural parameters. The total RMSE
is 88 ton/ha.
The methods developed in this study could be applied to estimate forest biophysical
attributes at regional or global scales. With optical remote sensing imagery, only forest
fractional cover and leaf area index could be estimated. When both optical and SAR data
are acquired, the forest structural parameters and aboveground biomass can be estimated.
These results could provide quantitative information in full carbon accounting in global
climate change studies.
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To
my husband Lanwu Zhao and my baby girl Jessie with all my love
And to
my family in china for their infinite support and encouragement
IV
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ACKNOWLEDGMENTS
First of all, I would like to thank Dr. Jiaguo Qi, my faculty advisor, for his constant help
and encouragements during my graduate studies. Without his academic guidance, it is
impossible for me to complete this dissertation and move forward to my academic career.
I would also like to thank my committee members. Dr. David Skole, Dr. David Lusch,
Dr. Richard Kobe, and Dr. William Salas for their support and help even since the
dissertation proposal was emerged.
I am also grateful to all the faculty and staff members in Department of Geography,
Michigan State University, who help me to possess the knowledge as a geographer as
quick as I can. Special thanks to Dr. Randall Schaetzl and Ms. Sharon Ruggles who gave
me the strongest support when I was struggling in US as a foreign graduate student.
Also, I would like to thank Dr. Mark Cochrine, Walter Chomentowski, Jay Samek,
Eraldo Matricardi, Oscar Castaneda and Cameron Williams in the Center of Global
Change and Earth Observations (CGCEO) for image processing, technical support and
helpful comments on my research. Many thanks go to Ms. Diane Cox and Deana Haner,
very wonderful ladies for their assistance in my study and research. I would also express
my appreciation to Narumon (Nok) Wiangwang, Pari (Perry) Vamakovida, Chetphong
Butthep in CGCEO, and Dr. Charlie Navanujraha, Ms. Siam Lawavirojwong and Woody
in Mahidol University, Bangkok, Thailand. Their warmhearted help and assistance in
field works in Thailand will never be forgotten.
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Finally, my deepest thank to my husband Lanwu Zhao who gave me endless support and
love during my graduate study. Also to my little sweetheart Jessie who was bom when I
began to write my dissertation. Her arrival motivated me to be a successful mother and
scientist. Thanks to my father, sisters, and brother in China for their love and many years’
support. This dissertation is also a very special memorial to my loved mother. I am sure
she will receive this message in heaven.
VI
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TABLE OF CONTENTS
Chapter 1
Introduction.............................................................................................................................. 1
1.1
Literature Review.......................................................................................................2
1.2
Research Obj ectives.................................................................................................. 6
1.3
References.................................................................................................................. 9
Chapter 2
Study Area and Field Measurements................................................................................. 12
2.1
Study A rea................................................................................................................12
2.2
Field Measurements.................................................................................................16
2.2.1
First field trip (August 10-18, 2001)............................................................ 16
2.2.2
Second field trip (January 20-27, 2002)...................................................... 18
2.2.3
Field data processing.......................................................................................20
2.2.3.1 Forest fractional cover............................................................................... 20
2.2.3.2 Stand density............................................................................................... 21
2.2.3.3 Aboveground biom ass............................................................................... 22
2.2.4
Ground data analysis.......................................................................................23
2.3
References................................................................................................................ 26
Chapter 3
Estimation of Tropical Forest Fractional Cover with Landsat ETM+ and IKONOS
Imagery....................................................................................................................................33
3.1
Introduction.............................................................................................................. 33
3.2
Remotely Sensed D ata............................................................................................ 37
3.2.1
Landsat ETM-t- im age.....................................................................................37
3.2.2
Higb-resolution IKONOS Images..................................................................41
3.3
Methods.....................................................................................................................42
3.3.1
A linear unmixing m odel............................................................................... 42
3.3.2
Optimal vegetation index (FT).......................................................................44
3.4
Canopy Fractional Cover Analysis........................................................................ 48
3.5
Validation..................................................................................................................49
3.5.1
Validation with ground measurements......................................................... 50
3.5.2
Validation with 1-m IKONOS data...............................................................53
3.5.3
Seasonal adjustment of fractional cover....................................................... 59
3.6
Conclusion and Discussion..................................................................................... 62
3.7
References.................................................................................................................67
Chapter 4
Estimation of Leaf Area Index with Fractional Cover Data in Tropical Forests
79
4.1
Introduction.............................................................................................................. 79
4.2
Experimental Design and Field Measurements.................................................... 81
4.2.1
LAI-2000 and fisbeye photographs...............................................................81
4.2.2
LAI ground measurements............................................................................. 84
4.2.3
LA I - forest fractional cover relationship.....................................................85
4.3
Model Development and Results............................................................................86
4.3.1
A modified Gaussian regression model........................................................ 86
4.2.2
LAI estimation................................................................................................. 87
Vll
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4.4
Validation.................................................................................................................89
4.5
Conclusion and Discussion.................................................................................... 90
4.6
Reference.................................................................................................................93
Chapter 5
A Microwave/Optical Synergistic Canopy Scattering Model and its Inversion to
Estimate Forest Structure..................................................................................................100
5.1
Introduction............................................................................................................100
5.2
Model Development.............................................................................................. 104
5.2.1
Modified Karam-IEM m odel........................................................................105
5.2.1.1 Soil surface scattering...............................................................................105
5.2.1.2 Leaf scattering........................................................................................... 107
5.2.1.3 Branch scattering...................................................................................... 108
5.2.1.4 Trunk scattering........................................................................................ 108
5.2.1.5 Leaf-soil interaction.................................................................................. 109
5.2.1.6 Branch-soil interaction..............................................................................109
5.2.1.7 Trunk-soil interaction................................................................................109
5.2.2
Linkage to optical remotely sensed variables............................................ 110
5.2.3
PDF functions of forest components............................................................ I l l
5.3
Model Simulation and Validation............................. .........................................112
5.3.1
Remotely sensed data.....................................................................................112
5.3.2
Model simulation........................................................................................... 116
5.3.2.1 Contribution of leaves............................................................................... 116
5.3.2.2 Contribution of branches...........................................................................117
5.3.2.3 Contribution of trunks............................................................................... 118
5.3.3
Model validation............................................................................................ 120
5.4
Model Inversion and Forest Parameters Estimation...........................................122
5.4.1
Model inversion............................................................................................. 122
5.4.2
Forest structural parameters by model inversion........................................ 123
5.4.3
Uncertainty analysis.......................................................................................124
5.5
Conclusions and Discussion.................................................................................127
5.6
References.............................................................................................................. 130
Chapter 6
Aboveground Woody Biomass Estimation with Microwave and Optical Remotely
Sensed Data........................................................................................................................... 145
6.1
Introduction............................................................................................................ 145
6.2
Biomass Estimation with a Simple Regression M ethod.................................... 149
6.3
Biomass Estimation with Compensation of leaf Attenuation............................150
6.4
Biomass Estimation with Synergistic Model and Allometric Equations
154
6.5
Conclusions and Discussions................................................................................157
6.6
R e fe re n c e s..............................................................................................................................161
Chapter 7
Conclusions and Future Envisions................................................................................... 172
7.1
Concluding Remarks............................................................................................. 172
7.2
Challenges.............................................................................................................. 176
7.3
Potential Applications for Other Studies.............................................................178
vm
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LIST OF TABLES
Table 2-1 Average values of ground measurements in different forest types.................. 27
Table 2-2 Average values of ground measurements in different forest types when the
outlier was deleted................................................................................................ 27
Table 4-1 Study sites in northern Michigan.........................................................................95
Table 5-1 System parameters of JERS-1 SAR and NVIR sensors...................................132
Table 5-2 Input parameters for model simulation..............................................................132
Table 5-3 Input parameters for model validation (in addition to ground measurements).
.............................................................................................................................133
Table 5-4 A set of forest structural parameters for each forest type in the model
133
Table 5-5 Average and standard deviation of modeled tree height, stand density, and the
error of model inversion for each forest types..................................................134
Table 6-1 Coefficients of cr“ -biomass logarithmic curve fitting and their statistic tests.
............................................................................................................................ 164
IX
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LIST OF FIGURES
Figure 2-1
Figure 2-2
Figure 2-3
Figure 2-4
Figure 2-5
Figure 2-6
Figure 2-7
Figure 2-8
Figure 2-9
The study area (Mae Chaem Watershed)............................................................28
The DEM data (with hill shade effect) in the study area...................................28
Vertical distribution of forest types in the study area........................................ 29
Forest type map in the study area........................................................................ 29
Study sites and grormd control points (GCPs) in the study area...................... 30
fisheye picture and its transformation in angular coordinates.......................... 30
Correlation scatterplot matrix of ground-measured data................................... 31
Normalized average ground measurements in different forest types............... 32
Modified normalized average ground measurements in different forest types
(the outlier of dry evergreen site was removed)................................................. 32
Figure 3-1 ETM+(band4+3+2, 02/02/2000) (a) and IKONS images (band4+3+2,
02/27/2000 and 10/09/2002) (b) in Mae Chaem Watershed. The dots in (b)
represent the study sites in two field trips...........................................................70
Figure 3-2 DEM data with hillshade effect (a) and topographic correction with Rahman’s
BRDF model (b)....................................................................................................71
Figure 3-3 Dynamic ranges of the six vegetation indices calculated with simulated
spectral data in SAIL model.................................................................................71
Figure 3-4 MSAVI image in Mae Chaem Watershed.......................................................... 72
Figure 3-5 Forest fractional cover map in Mae Chaem Watershed.....................................72
Figure 3-6 Comparison of ground-measured and ETM+ estimated fc: scatterplot (a) and
clustered column (b)............................................................................................. I'i
Figure 3-8 Scatter plots of ground-measured and the IKONOS estimated fc in 6 sites (a),
and the ETM+ and IKONOS estimated fc in 400 randomly selected points (b).
75
Figure 3-9 IKONOS and ETM-t subset images and their fc maps...................................... 76
Figure 3-10 The ground-measured, IKONOS and ETM-i- estimated fc scatterplot in dry
and wet seasons..................................................................................................... 77
Figure 3-11
The curve fitting model to adjust fc in dry season towet season................ 77
Figure 3-12 Forest fractional cover map adjusted from dry season to wetseason
78
Figure 4-1 Relationships between LAI (5-ring and 4-ring) with forest fractional cover.
The fractional cover is in the range of [0,1]........................................................96
Figure 4-2 Comparison of LAI measurements from LAI-2000 and fisheye Photos. The
1:1 line is also drawn in the plot..........................................................................96
Figure 4-3 LAI ~ forest fractional cover relationships measured with LAI-2000 and
fisheye photos in northern Michigan...................................................................97
Figure 4-4 A modified Gaussian curve fitting (a) and its residual plot (b)........................ 97
Figure 4-5 LAI distribution in the study area in dry (a) and wet (b) seasons.....................98
Figure 4-6 Comparison of modeled and adjusted measured LAI over the study sites in the
watershed (second trip).........................................................................................99
Figure 4-7 Correlation between modeled and adjusted measured LAI over the study sites
with LAI <6........................................................................................................... 99
Figure 5-1 Geometry o f a forest canopy.............................................................................. 135
Figure 5-2 PDF functions of leaves, branches, and trunks.................................................135
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Figure 5-3 JERS-1 SAR (a) and VNIR data (NIR+Red-^Green) (b) in the study area... 136
Figure 5-4 DEM data (a) and geometrically corrected JERS-1 SAR image (b).............. 137
Figure 5-5 Local incidence angle image (a) and the topographically corrected image (b).
The layovers and shadows are the white areas in (a)....................................... 137
Figure 5-6 Modeled backscattering of different forest components; leaf size (a), LAI (b),
branch radius (c), branch density (d), DBH (e), stand density (f), and tree
height(g)...............................................................................................................137
Figure 5-7 Backscattering simulation based on ground-measured forest parameters: LAI
(a), tree height (b), trunk height (c), DBH (d), and stand density (e).............139
Figure 5-8 Comparison of JERS-1 SAR observed and modeled backscattering
coefficients: scatterplot of backscattering coefficients (a) and residuals (b).
The 1:1 line is also drawn in (a).........................................................................140
Figure 5-9 Scatterplot of stem height and DBH to tree height measured at study sites. 141
Figure 5-10 Modeled tree height distribution in the watershed......................................141
Figure 5-11 Modeled stand density distribution in the watershed..................................142
Figure 5-12 Error distribution of model inversion...........................................................142
Figure 5-13 Scatterplots of modeled and measured tree height (a) and stand density (b)
at all study sites................................................................................................... 143
Figure 5-14 Comparison of modeled and measured tree height (a) and stand density (b)
in different forest types....................................................................................... 144
Figure 6-1 Scatterplot and curve fitting of
-biomass at all study sites.................165
Figure 6-2 Biomass distribution estimated with a simple
-biomass regression
model.................................................................................................................... 165
Figure 6-3 The Z-./1/distribution derived from JERS-1 VNIR data in the study area
166
Figure 6-4 Relationships between ground-measured biomass and modeled backscattering
coefficients of forests (a) and their components (b).........................................167
Figure 6-5 Logarithmic curve fitting of SAR observed and modeled backscattering
coefficients with biomass................................................................................... 168
Figure 6-6 Leaf scattering (a) and its attenuation to the woody forests (b)......................168
Figure 6-7 Woody scattering in the study area....................................................................169
Figure 6-8 Biomass distribution with woody forest scattering in a regression m odel... 169
Figure 6-9 Modeled DBH distribution in the watershed.................................................... 170
Figure 6-10 Forest biomass distribution from model inversion in the watershed
170
Figure 6-11
Scatterplot of modeled and measured biomass at study sites................... 171
Figure 6-12 Average values of measured and modeled biomass (without outlier) and
the standard deviation of the modeled biomass in each forest type................171
XI
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Chapter 1
Introduction
Forests are a major natural resource in the Earth biosphere and control a wide range of
environmental processes. Forest ecosystems play a vital role in energy, gas and water
exchanges between the land surface and the atmosphere. Tropical forests hold the largest
proportion of the Earth’s biodiversity, and account for approximately 40% of terrestrial
biomass (Foody et al. 1997). They play an important role in water, gas, and energy cycle
due to the geosphere, biosphere, and atmosphere interactions. Traditional forest mapping
with remote sensing imagery provide important information for the assessment of
deforestation, reforestation, and afforestation in tropical forests as a result of both human
activities and natural processes. But this forest/non-forest classification is a more
qualitative technique which introduces high uncertainty in full carbon accounting.
Therefore, we need continuous fields of biophysical distribution in tropical forests
(DeFries et al. 1999, 2000). Quantitative measure of forest area, density, biomass and
other biophysical attributes of tropical forests is necessary to fully assess the intensity and
extent of forest degradation, fragmentation, and recovery from human and natural
disturbances. These biophysical attributes are also important input parameters of the
carbon and climate models in global climate change studies. (Skole and Tucker 1993;
M>neni et al. 1998). This study thus aims to develop new algorithms to retrieve these
biophysical attributes in tropical forests with both optical and microwave remote sensing
techniques.
1
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1.1
Literature Review
The quantification of tropical forest variables is not an easy task. Due to labor and cost
constraints and the physical difficulties to access the study areas, field in-situ
measurements of biophysical attributes are very limited in dense tropical forests. The
limited ground measurements by destructive methods, litter collections, and sawmill
census data may not be representative when applied to large areas of tropical forests. As
an alternative, remote sensing techniques provide timely and spatially continuous
measurements over large areas and, therefore, have the potential to offer a timely and cost
effective estimate of forest biophysical attributes (Uhl 1987).
Optical remote sensing records the energy of sunlight reflected from the Earth surface in
the wavelength range of ~0.4pm (visible) to ~14.0pm (thermal infra-red), comparable to
the size o f atmospheric components such as aerosol and water vapor. These components
absorb and scatter sunlight and therefore reduce the quality of optical imagery. Tropical
forests are often covered by clouds which precludes information extraction from optical
remote sensing imagery. Aside from atmosphere effects, the spectral responses of optical
images are strongly dependent on the green leaf characteristics of the forest canopy. The
reflected energy from a forest canopy is determined by the physical properties of the
foliage such as chlorophyll concentration, internal leaf structure, the lignin component of
the cell walls, and the water content. Since the spectral responses of branches and trunks
are similar to those of bare soil surfaces, reflectance is not very sensitive to these woody
components in forests.
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Microwave remote sensors use a different range of the electromagnetic spectrum from
optical remote sensors. The spectral wavelengths of the Synthetie Aperture Radar (SAR)
most commonly used in forest studies range from 3cm (X-band) to 30cm (P-band). These
wavelengths are much longer than the sizes of the atmospheric constituents so the
atmosphere is transparent to microwave remote sensing. The capability of cloud
penetration is a great advantage of microwave remote sensing in tropical forest studies.
At certain system parameters, the intensity and phase of a radar backscatter signal is
mostly determined by dielectric constants and forest structures. The radar backscatter is
also dependent on the wavelength of the signal. Signals with shorter wavelength (e.g. Cor X-band) mainly scatter from erown layer. Longer wavelength signals (e.g. L- and Pbands) can penetrate the crown layer and reach the soil surfaee underneath. Therefore, the
backscattered SAR signals contain the information of woody components like branehes
and trunks in forests.
The biophysical attributes of green canopies have been a primary interest of the optical
remote sensing community. Various approaches have been developed to estimate these
attributes like forest fractional cover (Price 1992, Wittich 1997, Gutman and Ignatov
1998, Qi et al. 2000) and leaf area index (Peterson et al. 1987, Pinty et al. 1990, Qi et al.
1995). The high temporal (daily through yearly) and eoarse spatial (250m to 1km)
resolution products of these biophysical attributes on a global seale were also developed
with the MODIS and MISR data aboard the Earth Observation System satellites
(Townshend et al. 1999; DeFries et al. 2000).
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The attempt o f using microwave remote sensing to retrieve biophysical attributes began
in early 1960s. In May of 1960, the Terrain Handbook by Professor Bill Peake was
published which denoted a major transformation of radar remote sensing from qualitative
image interpretation to quantitative information retrieval (Ulaby 1998). During the past
decade, the application of SAR imagery in monitoring ecosystem processes has grown
significantly (Kasiscbke et al. 1997). Various types of SAR data, including single­
frequency, single-polarization, multi-frequency, multi-polarization, multi-temporal,
polarimetric and interferometric data, have been acquired and subsequently applied to
map forest areas and estimate biophysical attributes such as biomass (Dobson et al. 1992,
Le Toan et al. 1992, Luckman et al. 1998), forest structure (McDonald and Ulaby 1993,
Imboff 1995, Sun and Ranson 1998), and soil moisture (Ob et al. 1992, Dubois et al.
1995, Shi et al. 1997). However, applications of microwave imagery are limited because
of the intrinsic speckle effects in SAR images and the topographic influence in
mountainous areas (Henderson and Lewis, 1998). Salas et al. (2002a, b) concluded that
although quantitative estimates of biomass are not stable due to intrinsic texture, system
noise, and environmental effects, multi-temporal analysis significantly improves biomass
estimates in secondary tropical forests.
Among the studies mentioned above, most of the forest biophysical estimation was
carried out using either optical or microwave remote sensing. For example, leaf area
index and forest fractional cover are estimated with optical remote sensing imagery,
while aboveground biomass, structure, and soil moisture are estimated with microwave
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remote sensing imagery. Due to the different wave-target interaction mechanisms, each
type of imagery has its own advantages and disadvantages. Optical remote sensing is very
sensitive to vegetation chlorophyll, wetness, and leaf structures, but is less sensitive to
branches or trunks. Microwave remote sensing is sensitive to biomass and woody
structures, but highly affected by the attenuation from leaf canopies.
A synergistic use of optical and microwave remote sensing could fill the application gaps
so as to improve the estimation of forest biophysical attributes. A simple way of
optical/SAR synergism is image fusion that can be performed at the pixel level in which
physical measurements of sensors are merged (Nezry et al. 1993); at the feature level in
which characteristics of the original images are merged (Solberg et al. 1994); and at the
interpretation level (also called decision level) in which the labeled features are merged
(Rignot et al. 1996). At higher level of synergism, the canopy reflectance and scattering
models could be investigated and combined with remotely sensed data to simulate surface
conditions. However, it is not an easy task because of the very different mechanisms of
signal-surface interactions.
Only limited effort has been made on the synergy of optical and microwave remote
sensing. Moran et al. (2002) used Landsat TM imagery to eliminate vegetated and/or
moist fields when investigating the sensitivity of ERS-2 data to surface roughness of bare
soil. Rignot et al. (1996) combined the classifications from TM and L-band SIR-C
images to achieve a higher accuracy of land cover and deforestation/regrowth mapping.
Salas (2002a) used both TM and JERS-1 data to map land use and land cover change, and
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to assess deforestation and secondary vegetation in tropical forests. Moghaddam and
Dungan (2000) attempted to estimate foliage biomass by fusing SAR and TM data at the
pixel level. All o f these studies are based on optical and SAR image fusions and their
results are mostly qualitative. These methods are far too simple to be applied in
quantification of forest biophysical attributes.
1.2
Research Objectives
This study aims to develop new algorithms to estimate specific biophysical attributes
using a micro wave/optical radiative transfer model and both optical and SAR remotely
sensed data. More specifically, the following detailed objectives will be addressed:
1)
Develop improved approaches to estimate forest canopy fractional cover using
optical remote sensing at medium to coarse resolutions and validate these
results with high resolution IKONOS imagery and ground data.
2)
Relate leaf area index distribution to the forest fractional cover product.
3)
Develop a microwave/optical synergistic canopy scattering model to simulate
scattering from all forest components and to estimate forest structural
parameters by model inversion using JERS-1 SAR/VNIR data.
4)
Map aboveground woody biomass distribution with the microwave/optical
synergistic model and JERS-1 SAR/VNIR data.
The dissertation is outlined as follows:
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Chapter 2 describes the study area and ground data measured during the field trips. The
biophysical attributes measured at the study sites include forest fractional cover, tree
height, stem height, diameter at breast height (DBH), tree age, stand density and stand
biomass.
Chapter 3 focuses on the estimation of forest fractional cover with optical remote sensing
data. A linear unmixing model in vegetation index domain was built and the forest
fractional cover distribution was mapped in the study area. The fractional cover map was
validated with both ground measurements and high resolution (1-m) IKONOS data. This
chapter fulfills Objective 1.
Chapter 4 describes the estimation of leaf area index with the results of forest fractional
cover retrieved in Chapter 2. A third field trip was carried out to simultaneously measure
leaf area index with a LAI-2000 instrument, and forest fractional cover with fisheye
photos using GLA software. A modified Gaussian regression model was built to relate
leaf area index to forest fractional cover. The model was examined with a
goodness-
of-fit test. Then the leaf area index distribution was mapped with the forest fractional
cover product in the study area. This chapter fulfills Objective 2.
Chapter 5 focuses on the estimation of forest structural parameters in the study area. A
microwave/optical synergistic canopy scattering model was built to simulate the
scattering from all of the forest components. The leaf scattering and its attenuation to
other components were quantified with the leaf area index retrieved in Chapter 3, so that
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only the woody structures, such as tree height, stand density, and diameter at breast
height (DBH) were the unknown variables in the model. With JERS-1 SAR and VNIR
data, the model was inversed and the forest structures estimated. This chapter fulfills
Objective 3.
Chapter 6 describes three different methods in the estimation of aboveground woody
biomass in the study area. First, a simple regression model was built to estimate biomass
from JERS-1 SAR backscattering coefficients. Second, another regression model was
built to estimate biomass from woody scattering when the leaf scattering and its
attenuation to woody components (branches and trunks) was quantified with JERS-1
VNIR data. Finally, according to allometric equations, the woody biomass was also
calculated using the forest structure parameters estimated in Chapter 4. This chapter
fulfils Objective 4.
Chapter 7 contains the conclusions and challenges of this study. Its potential applications
in land use and land cover change and carbon and global climate change studies are also
addressed.
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1.3
References
DeFries, R. S., Townshend, J. R. G. and Hansen, M. C. (1999). Continuous fields of
vegetation characteristics at the global scale at 1-km resolution. Journal of
Geophysical Research. 104, 16,911-16,923.
DeFries, R. S., Hansen, M. C., Townshend, J. R. G., Janetos, A. C. and Lovelands, T. T.
(2000). A new global 1-km dataset of percentage tree cover derived from remote
sensing. Global change biology: 6, 247-254.
Dobson, M. C., Ulaby, F. T., Le Toan, T., Beaudoin, A. and Kasiscbke, E. S. (1992).
Dependence of radar backscatter on coniferous forest biomass. IEEE Transactions
on Geoscience and Remote Sensing: 30,412-415.
Dubois, P. C., Van Zyl, J., and Engman, E. T. (1995), Measuring soil moisture with
imaging radar. IEEE Transactions on Geoscience and Remote Sensing: 33(4):
915-926.
Foody, G. M., Lucas, R. M., Curran, P. C., and Honzak, M. (1997). Mapping tropical
forest fractional cover from coarse resolution remote sensing imagery. Plant
Ecology, Vol. 131, 143-154.
Gutman G. and Ignatov, A. (1998). The derivation of the green vegetation fraction from
NOAA/AVHRR data for use in numerical weather prediction models.
International Journal of Remote Sensing: 19(8), 1533-1543.
Henderson, F. M. and Lewis, A. J. (1998). Principles and applications of imaging
RADAR, Manual of remote sensing, vol.2, third edition. John Wiley &Sons, New
York.
Imboff, M. L. (1995). A theoretical analysis of the effect of forest structure on Synthetic
Aperture Radar backscatter and the remote sensing of biomass. IEEE
Transactions on Geoscience and Remote Sensing: 33(2), 341-352.
Kasiscbke, E. S., Melak, J. M. and Dobson, C. (1997). The use of imaging radars for
ecological applications - a review. Remote Sensing of Environment: 59, 141-156.
Le Toan, T., Beaudoin, A. and Guyon, D. (1992). Relating forest biomass to SAR data.
IEEE Transactions on Geoscience and Remote Sensing: 30, 403-411.
Luckman, A., Baker, J., Honzak, M. and Lucas, R. (1998). Tropical forest biomass
density estimation using JERS-1 SAR: seasonal variation, confidence limits, and
application to image mosaics. Remote Sensing of Environment: 63, 126-139.
McDonald, K.C. and Ulaby, F. T. (1993). Radiative transfer modeling of discontinuous
tree canopies at microwave frequencies. International Journal of Remote Sensing:
14(11), 2097-2128.
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Moghaddam, M., and Dungan, J. (2000). Fusion of SAR and TM data for Quantitative
estimation of forest variables over an extended range of validity. Proceedings of
IEEE International Geoscience and Remote Sensing Svmposium. 24-28, July,
Honolulu, Hawaii.
Moran, M. S., Hymer, D. C., Qi, J. and Kerr, Y. (2002). Comparison of ERS-2 SAR and
Landsat TM imagery for monitoring agricultural crop and soil conditions. Remote
Sensing o f Environment: 79, 243-252.
Myneni, R. B., Tucker, C. J., Asrar, G. and Keeling, C. D. (1998). Interannual variations
in satellite-sensed vegetation index data from 1981 to 1991. Journal of
Geophvsical Research: 103(D6), 6145-6160.
Nezry, E., Mougin, E., Lopes, A. and Gastellu-Etchegorry, J. P. (1993). Tropical
vegetation mapping with combined visible and SAR spacebome data.
International Journal of remote sensing: 14 (II), 2165-2184.
Oh, Y., Sarabandi, K., and Ulaby, F. T. (1992), An empirical model and an inversion
technique for radar scattering from bare soil surfaces. IEEE Transactions on
Geoscience and Remote Sensing: 30(2):370-381.
Peterson, D. L., Spanner, M. A., Running, S. W. and Teuber, K. B. (1987). Relationship
of thematic mapper simulator data to leaf area index of temperate coniferous
forest. Remote Sensing of Environment: 22, 323-341.
Pinty, B., Verstraete, M. M. and Dickinson, R. E. (1990). A physical model of the
bidirectional reflectance of vegetation canopies, 2: Inversion and validation.
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Price, J.C. (1992). Estimating vegetation amount form visible and near infrared
reflectances. Remote Sensing of Environment: 41, 29-34.
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using multidirectional spectral measurements. Remote Sensing of Environment:
54, 71-83.
Qi, J., Marsett, R. C., Moran, M. S., Goodrich, D. C., Heilman, P., Kerr, Y. H., Dedieu,
G., Chehbouni, A. and Zhang, X. X. (2000). Spatial and temporal dynamics of
vegetation in the San Pedro River basin area. Agricultural and Forest
Meteorologv: 105, 55-68.
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growth in Rondonia, Brazil using imaging radar and Thematic Mapper data.
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Salas, W.A., Dueey M. J., Eignot, E. and Skole, D. (2002a). Assessment of JERS-1 SAR
for monitoring secondary vegetation in Amazonia: I. Spatial and temporal
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variability in backscatter across a chrono-sequence of secondary vegetation stands
in Rondonia. International Journal of Remote Sensing: 23 (7), 1357-1379.
Salas, W.A., Ducey M. J., Eignot, E. and Skole, D. (2002b). Assessment of JERS-1 SAR
for monitoring secondary vegetation in Amazonia: II. Spatial, temporal, and
radiometric considerations for operational monitoring. International Journal of
Remote Sensing: 23(7), 1381-1399.
Shi, J. C., Wang, J., Hsu, A. Y., O’Neil, P.E., Engman, E. T. (1997), Estimation of bare
surface soil moisture and roughness parameters using L-band SAR image data.
IEEE Transactions on Geoscience and Remote Sensing: 35:1254-1265.
Skole, D., and Tucker, C. J. (1993). Tropical deforestation and habitat fragmentation in
the Amazon: satellite data from 1978 to 1988. Science: 260, 1905-1910.
Solberg, A. H., Jain, A. K. and Taxt, T. (1994). Multisource classification of remotely
sensed data: fusion of Landsat TM and SAR images. IEEE Transactions on
Geoscience and Remote sensing: 32, 768-777.
Sun, G. and Ranson, K. J. (1998). Radar modeling of forest spatial patterns. International
Journal o f Remote Sensing: 19(9), 1769-1791.
Townshend, J. R. G. (1999). MODIS enhanced land cover and land cover change
product: algorithm theoretical basis documents (ATBD), version 2.0.
Uhl, C. (1987). Factors controlling succession following slash-and-bum agriculture in
Amazonia. Journal of Ecologv: 75, 377-407.
Ulaby, F. T. (1998). SAR biophysical retrievals: lessons learned and challenges to
overcome. In Proceedings of Retrieval of Bio- and geophysical parameters from
SAR data for land applications, ESTEC, NL, October.
Wittich, K. P. (1997). Some simple relationships between land-surface emissivity,
greermess and the plant cover fraction for use in satellite remote sensing.
International Journal of Biometeoroloav: 41, 58-64.
II
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Chapter 2
Study Area and Field Measurements
2.1
Study Area
The study area is in the topical forests in north Thailand, located at 98°00’ - 99°00’E and
18°00’ - 19°00’N. Chiang Mai, the second largest city in Thailand, is located in the lower
flatlands in the east o f the watershed. The study area consists of four sub-watersheds:
Mae Chaem, Mae Wang, Mae Samoeng, and Mae Klang (Figure 2-1), covering an
approximate area of 6,692 sq. km. It is called the greater Mae Chaem Watershed in this
study. The watershed encompasses a wide range of topography, climate, vegetation types
and density, and human disturbance. Rich historical field survey data, such as land use /
land cover (LULC), soil texture, streams, and elevation, have been made available
through past research activities.
The topography varies greatly within the study area. The altitude ranges from 250 to
2,500 meters above the sea level. Mount Doi Inthanon (2565.33m) at the center of the
study area is the highest point in Thailand. The digital elevation data (DEM at 30-m
resolution) covering the whole watershed (Figure 2-2) was acquired during the past
research projects in cooperation with Dr. Charlie Navanujraha and Mr. Siam
Lawavirojwong in Mahidol University, Bangkok, Thailand. The low-elevation flat area in
the southeast is connected to the city of Chiang Mai, and the one in the middle is Mae
Chaem town. With limited urban settlements, the focus study area is generally above 500
meters in elevation.
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The climate in the study area is tropical monsoon that consists of southwest (SW) and
northeast (NE) monsoons. The SW monsoon starts in mid-May, ends in mid-November,
and is characterized by moist winds from the Andaman Sea as the main source of rainfall
during this period. The SW monsoon season is also called the wet season in the study
area. During the wet season, there are two peaks of rainfall periods: June-August and
September-October (Dr. Charlie Navanujraha, Mahidol University, personal
communication). The NE monsoon starts in mid-November, ends in mid-February, and is
characterized by cold dry winds from Siberia. The NE monsoon season is also called the
cold season in the study area. Both the cold season and the period between NE and SW
monsoons (March-June) compose the dry season as little rainfall occurs.
The general land cover types in the study area include forest, agriculture, grass
land/savanna, open land/bare soil, urban/settlement, and water bodies. Tropical forests
cover 80% o f the area (Thailand LUCC case study, 1997). As outlined in Figure 2-3,
tropical forests can be categorized into five types based on the elevation: dry dipterocarps
(lower and higher), mixed deciduous, Pine transition. Tropical dry (lower) evergreen, and
tropical moist (upper) evergreen. Moist evergreen forest is also called cloud forest
because it is often covered by clouds throughout the year. Except for the Teak plantation
around the Ecotourism Forest Station, Royal Department of Forestry, Thailand, in the
southwest of the study area, large-size plantations were not observed. Aside from these
five major forests, the evergreen gallery is composed of rain evergreen forest. Although
at the same elevation with dry dipterocarps (Figure 2-3), rain evergreen forest is confined
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to narrow damp valleys mixed with dense bamboo. The spatial distribution of the
evergreen gallery is very limited in the study area and, therefore, was not surveyed. The
buffer zone in Figure 2-3 is the area of human settlement such as villages, open land, and
agricultural fields at lower elevations. The pictures of different forest types are also
shown in Figure 2-3.
A forest type map was made by visual interpretation of a pan-sharpened ETM+ image
acquired on February 2, 2000 (Figure 2-4). It was examined with ground surveys,
historical land cover maps, and high-resolution IKONOS imagery. Only dry dipterocarps,
mixed deciduous, and tropical evergreen forests were categorized in the forest type map.
Pine transition is a narrow zone distributed between the mixed deciduous and tropical
evergreen forests and was not mapped in Figure 2-4. The spectral signature of dry
evergreen is very similar with that of moist evergreen so that they cannot be separated
from ETM+ imagery.
Dry dipterocarps are deciduous forests generally located at low elevations. They can also
be divided into lower and higher dry dipterocarps that are subtly different in senescent
and leaf-off time. Common species of dry dipterocarps primarily include Rokfar, Okfar,
Dang, Makarm, Teak, and Theng (personal commimication with foresters at local areas).
In areas close to the human settlements, forests suffer frequently from short-term clear
cutting and burning for agriculture. After intense cultivation for several years, these
agriculture fields are often abandoned. As a consequence, dry dipterocarps re-grow in
these areas. Most of the dry dipterocarp forests in the Mae Chaem Watershed are
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secondary regrowth forests disturbed by fire and logging. The stand density and
fractional cover of dry dipterocarps are low.
Mixed deciduous forests occur at higher elevations. The species of both deciduous and
evergreen forests primarily include Plung, Mako, Tueng, and Sompee (personal
communication with foresters at local areas). Mixed deciduous forests also suffer from
logging disturbance. However, due to the topographical difficulty to access these
elevations, the degree of disturbance is lower than that of dry dipterocarp forests.
Pine transition is a narrow zone distributed at an approximate elevation of 600-700m. The
occurrence of pine trees is an indicator that the forest distribution shifts from mixed
deciduous to tropical evergreen forests. The species are a mixture of Pine, Ko, Sompee,
and Sarapee (personal communication with foresters at local areas). Pine transition zone
is narrow and discontinuous, and its spectral signature is very similar to that of tropical
evergreen forests. It is difficult to identify pine transition from evergreen forests in ETM+
images. There is no natural pine forest in the Mae Chaem Watershed.
Tropical evergreen forests occur at higher elevations. They can be further divided into
tropical dry evergreen and moist evergreen. Tropical dry evergreen forests are distributed
just above the pine transition zone. The primary species include Ko, Taklan, Meng,
Sarapee, and Yaung (personal communication with foresters at local areas). Tropical
moist evergreen forests are found on the very top of mountains where the forests are
often covered by cloud and the microclimate makes it moist year round. Sor and Ko are
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the primary species of tropical moist evergreen forests in the study area. Due to the
extreme difficulty to access these areas, tropical evergreen forests are generally dense
natural forests, almost undisturbed by human activities.
2.2
Field Measurements
2.2.1 First field trip (August 10-18, 2001)
The first field trip was made in the wet season of the study area. This field trip was a
partially follow-up program of the Southeast Asia Regional Research and Information
Network (SEARRIN) science meeting & workshop held in August 2001 in Chiang Mai,
Thailand. The trip included one day of training, three days of field survey, and five days
of field measurements. There were three ground measurements on the training day and
twelve measurements on the following days. The study sites were distributed from
Chiang Mai to Mae Chaem town along the Doi Inthanon 1009 Royal Road. These study
sites covered the following forest types: dry dipterocarps, mixed deciduous, pine
transition, and moist evergreen forests. No dry evergreen forests were measured during
this trip.
On the training day, scientists from the countries in Southeast Asia, who are the
participants of the SEARRIAN workshop, made a field tour from Chiang Mai to Mount
Doi Inthanon. Dr. David Skole, Dr. Jiaguo Qi, and Mr. Jay Samek of the Center of Global
Change and Earth Observations (CGCEO) at Michigan State University, which is the
organizer of the SEARRIAN workshop, demonstrated the technique of using a digital
camera with a fisheye lens to measure forest fractional cover. Three sites of dry
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dipterocarps, transitional forest, and upper tropical evergreen forest (moist evergreen)
were visited and fisheye pictures were taken on that day.
In the next three days, a field survey was made guided by Dr. Charlie Navanujraha. Two
transects were surveyed. One extended from Chiang Mai to the Ecotourism Forest Station
of the Royal Department of Forestry, Thailand (RDFT) located in the southwest of the
study area. The other extended from the Ecotourism Forest Station to the north of the
study area. During the field survey, forest types and their distributions were identified
and ground control points (GCPs) were selected for geometric image processing. Since it
was at the peak of raining season, only paved roads were accessible and there were
landslides all over the study area. The lengths of the two survey transects were thus
limited. The study sites were also selected during the field survey for intense
measurements later.
Based on the experiences during the field survey, detailed ground measurements at
selected sites were made in the following five days as a joint effort of Ms. Nok
Wiangwang, Mr. Perry Namakovida and myself (all associated with CGCEO, Michigan
State University), and forester Pluem from the Ecotourism Forest Station (RDFT). The
measured study sites were all located not far from the paved roads because of
accessibility issues. At each study site, one transect (150m long) perpendicular to the
paved road was made. To reduce the possibility of human disturbance, the first point was
at least 100m off the road.
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At each point, a fixed-radius plot method was used to measure tree biophysical
parameters. Centered at the point, a circular area with a radius of 5 meters was
established and the tree height, stem height, and diameter at breast height (DBH) of each
tree in the circle were measured. The measurements were limited to the trees higher than
2 meters and DBH larger than 10 centimeters. The fixed-radius plot method is very timeconsuming, but accurate for stand density and biomass measurements.
2,2,2
Second field trip (January 20-27,2002)
To collect more ground data and examine the temporal change in the study area, a second
field trip was made in the dry season in January 2002 by Mr. Siam Lawavirojwong,
Wood from Mahidol University and myself. The leaves of broadleaf forests, such as dry
dipterocarps and some mixed deciduous forests, had turned reddish/brownish and were
partially off. The leaves of the evergreen forests did not change much. Since there was
little precipitation in the dry season, and the grasses/shrubs underneath the forests were
senescent, more sites in the study area were accessible even from the dirt roads or trials in
the study area.
During this trip, twelve study sites from the first trip were re-visited and twenty new
study sites all over the watershed were measured. At the study sites where the biophysical
attributes had been measured during the first trip, only fisheye pictures were taken. At
each of the new sites, a 300m transect perpendicular to the road or trial was made
consisting of 10 points. The first point was at least 100m off the road to reduce the effect
of human disturbance. At each point, one fisheye picture was taken and a point-quadrant
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method was used to measure stand density. Centered at this point, four quadrants were
divided along and perpendicular to the transect direction. The nearest tree in each
quadrant was chosen and the tree height, stem height, DBH, and its distance to the center
were measured. Based on the forester’s expertise, the tree age and species were also
recorded. Again, only those trees higher than 2 meters with a DBH larger than 10
centimeters were measured. The point-quadrant method was more efficient than the
fixed-radius method used in the first trip. However, the accuracy is lower than the fixedradius method because only four trees are used to represent the forest conditions at each
point. It may introduce some bias to the ground measured biophysical attributes.
During the two field trips, a total of 32 study sites were measured. Fifteen measurements
(12 with complete measurements, 3 with fisheye pictures only) were made in the wet
season and thirty-two measurements (20 with complete measurements, and 12 with
fisheye pictures only) were made in the dry season. These study sites were distributed
across the watershed (Figure 2-5).
At each study site, the GPS reading at the nearest open area was recorded (in UTM
coordinates) and the offset to the first point of measurement was estimated. The exact
locations of study sites were adjusted based on the GPS reading and its offset. In
addition, during the field surveys in both trips, GPS readings of approximately 60 ground
control points (GCPs) were collected at distinct locations, such as the curve of a road, the
middle o f a bridge, and the comer of a reservoir. These GCP points were also distributed
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across the watershed (Figure 2-5) and were used for the geometric correction of the
remote sensing imagery.
2.2.3
Field data processing
During both field trips, tree species, age, tree height, stem height (the height between
ground and first branch), and DBH (diameter at breast height) were measured and fisheye
pictures were taken at the selected plots at each study site. The canopy height is the
difference of tree height and stem height. The canopy fractional cover, stand density and
stand biomass of each study site were also ealeulated from the measured data.
2.2.3.1
Forest fractional cover
Forest fractional cover (/c) is the percentage of tree canopy in unit area on the ground. At
each point along the transect o f the study site, forest fractional cover was calculated from
the fisheye pictures taken skyward from the forest floor with a digital camera and 180°
hemispherical (fisheye) lens. The pictures were eircular images that recorded the size,
shape, and location of gaps in the forest overstory (Figure 2-6a).
When the fisheye pictures were taken, the camera was held higher than the
photographer’s head to avoid the non-vegetation effect on the ground from people and
understory vegetation or rocks. It was also kept horizontal with a two-dimensional level.
Topographic shading is an inevitable effect in mountainous areas where ineident solar
radiation is influenced by not only surface orientation, but also a reduced view of the sky
hemisphere. In this study, most of the study sites were selected in the relatively flat or
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shallow-slope areas to reduce the topographic shading effect. For the few sites with steep
slopes, only zenith angles of 0°-80° were selected so that the areas affected by
topography were blocked out.
The hemispherical digital pictures were analyzed and fc calculated using the Gap Light
Analyzer (GLA) software (Frazer et al. 1999). The pixel positions in circular images
were transformed into angular coordinates (10° interval in both azimuth and zenith
directions) (Figure 2-6b). The pixel intensities were classified into sky and non-sky
classes. The gap fraction was the percentage of sky brightness throughout the whole
image. The forest fractional cover was equal to (100.0- gap fraction) in the range of 0100%. The fc value at each study site was the statistical average of total points in the
transect.
2.2.3.2
Stand density
The fixed-radius plot method was used during the first trip. At each study site, the plot
density at one point, S^, was the total number of trees divided by the area o f the plot (r =
5m). The stand density at the study site, S, was the statistical average of the five plot
densities along the transect:
# o f trees
7JT
(2 - 1)
n
where n is the number of points along each transect (n=5 in the first trip), and r is the
radius of the plot.
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The point-quadrant method was used during the second trip. At each study site during
this trip, the plot density at one point, S i, and the stand density S were calculated as:
S, =
+
d^ 4 d^
-
(2-2)
s = -i----n
where n is the number of points in each transect (n = 10 for most of the study sites in the
second trip), and d^, d^, d^^, d^ are the distances of the nearest tree to the origin in the
quadrants o f upper right, lower right, upper left, and lower left, respectively.
2.2.3.3
Aboveground biomass
The aboveground biomass, also called biomass density, is the total biomass of trees with
diameter > 10cm, including leaves, twigs, branches, hole, and bark (Brown et al., 1989).
For this study, the aboveground biomass included only the woody biomass. The leaves
are represented by the leaf area index and are not considered in the biomass calculation.
The stand biomass could he deduced from the total volume of the trees. When the trading
volume timber (i.e., trunk including wood and bark) is considered, the trunk volume of
one tree,
, can be expressed in a first approximation as (Le Toan et al., 1992):
Vi = T -n -
■H
(2-3)
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where ^ is the tree height, and T is the shape factor. T= 0.45 was applied in tropical
forests (Le Toan et al. 1992). Similarly, the stand volume at each study site ( F J was
calculated using:
V=T-
k
-
H S
(2-4)
where DBH and H are the statistical means of DBH and tree height. S is the stand
density as defined in section 2.2.3.2. Defining the mean dry wood density (including
bark) as p , the stand biomass at each study site {B^) was then calculated by:
B .= P -V ,
(2-5)
In tropical forests, the dry wood density is around 610 kg/m^ (Luckman et a l, 1997).
2.2.4
Ground data analysis
The ground based geophysical and biophysical parameters at the 32 study sites are
analyzed in this section. The histograms of each parameter and the relationships among
them are shown in the correlation scatterplot matrix (Figure 2-7). The 95% confidence
ellipses are also drawn in the scatterplots.
For tree age, tree height, DBH, canopy height and biomass, there is a positive correlation
with elevation. There is no correlation between tree density and elevation. The
biophysical parameters have high correlations with tree age. Tree height, DBH, canopy
height, and biomass are highly positively correlated with tree age. Tree density is also
correlated with tree age, but the trend is negative, indicating that young trees in secondary
forests have higher density, while old trees in primary forests have lower density. The
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tree height, canopy height, and DBH parameters are strongly positively correlated with
each other. Tree density has very weak correlations with the other biophysical parameters
except tree age as discussed above.
As shown in the histograms in Figure 2-7, none of the biophysical parameters at the study
sites are normally distributed. Except canopy height and tree age, all other parameters are
left-skewed, indicating more young trees and fewer old trees at the study sites. This bias
is especially obvious for stand biomass calculated from the measured biophysical
parameters using allometric equations.
Among the 32 study sites with complete measurements, there was 1 teak plantation, 8 dry
dipterocarps, 8 mixed deciduous, 1 rain evergreen (evergreen gallery), 5 pine transition, 6
dry evergreen, and 3 moist evergreen forest types. The teaks are also deciduous species
and belong to the dry dipterocarp forest type. The rain evergreen forest belongs to mixed
deciduous forests. The average of the biophysical parameters of the different forest types
is listed in Table 2-1. To make it comparable, the values in Table 2-1 were normalized to
[0,1] based on the maximum and minimum values for each parameter. This comparison is
shown in Figure 2-8.
Except tree density, the biophysical parameters values of dry dipterocarps, mostly young
secondary forests, were lowest. Moist evergreen forests are the primary and mostly
undisturbed forests and have the highest values for all biophysical parameters. The mixed
deciduous, pine transition and dry evergreen forests have biophysical values in between.
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Among the 6 dry evergreen study sites, only one site (S2-12) was young regrowth forest.
The other sites were degraded or undisturbed primary evergreen forests. However, from
Table 2-1 and Figure 2-8, the biophysical values of dry evergreen forests have a similar
range to those of mixed deciduous and pine transition. Their DBH values were even
lower than that of mixed deciduous forests that were mostly disturbed primary forests or
old secondary regrowth. If we treat Site 82-12 as an outlier and exclude it from the
statistical analysis, the normalized average (ranging from 0 to 1) of the different forest
types is shown in Figure 2-9. The biophysical parameters of the dry evergreen sites have
slightly higher values than those in Figure 2-8. The values in Figure 2-9 are shown in
Table 2-2 and will be used in the data analysis in the following chapters.
The biophysical parameters of dry evergreen forests in Figure 2-9 are still
underestimated. The possible reason for this underestimation comes from the method
used for the measurements. All of the dry evergreen study sites were measured during the
second field trip in which a point-quadrant plot method was used, whereas a fixed-radius
plot method was used in the first field trip. The point-quadrant plot method provides
efficient ways to measure biophysical attributes in each plot, but the accuracy is lower
than the fixed-radius method. In forests in late succession with higher heterogeneity,
there are more young trees surrounded the old parent tree. Therefore, young trees have a
much higher probability o f being picked in each quadrant which results in underestimated
measurements. Further data analysis and possible compensation for the point-quadrant
measurements need to be done in future research.
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2.3
References
Brown, S., Gillespie, A. J. R. and Lugo, A. E. (1989). Biomass estimation methods for
tropical forests with applications to forest inventory data. Forest Science: 35(4),
881-902.
Frazer, G. W., Canham, C. D. and Lertzman, K. P. (1999). Gap Light Analyzer (GLA),
Version 2.0: Imaging software to extract canopy structure and gap light
transmission indices from true-color fisheye photographs, users manual and
program documentation. Copyright © 1999; Simon Fraser University, Burnaby,
British Columbia, Canada, and the Institute of Ecosystem Studies, Millbrook,
New York, USA.
Le Toan, T., Beaudoin, A. and Guyon, D. (1992). Relating forest biomass to SAR data.
IEEE Transactions on Geoscience and Remote Sensing: 30, 403-411.
Luckman, A., Baker, J., Kuplich, T. M., Yanasse, C. C. F. and Frery, A. C. (1997). A
Study of the relationship between radar baekscatter and regenerating tropical
forest biomass for spacebome SAR instruments. Remote Sensing of Environment:
60, 1-13.
26
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Table 2-1
Average values of ground measurements in different forest types.
tree H (m) stem H (m) DBH (cm) density (#/ha) canopy H (m) biomass (ton/ha)
48.510
17.242
711.871
5.775
10.624
7.736
dipt
85.032
544.754
7.316
22.899
13.298
9.640
mixed
170.128
24.564
754.859
8.105
10.212
pine trans
14.265
105.493
21.704
7.956
10.179
605.443
dry ever
14.157
436.373
777.622
9.809
17.121
35.340
moist ever
22.025
Table 2-2
Average values of ground measurements in different forest types when the
outlier was deleted.
tree H(m) stem H (m) DBH cm) density (#/ha) canopy
17.242
711.871
7.736
dipt
10.624
22.899
544.754
9.640
mixed
13.298
24.564
754.859
10.212
pine trans
14.265
10.262
23.338
581.271
dry ever
14.489
777.622
17.121
35.340
moist ever
22.025
H (m) biomass ton/ha)
47.756
5.775
92.713
7.316
95.786
8.105
200.409
8.454
426.682
9.809
27
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Mae Chaem Watershed
M ae C haem
M ae Sam oenc
Thailaritfmap
M ae Klanc
‘V )
Figure 2-1
The study area (Mae Chaem Watershed).
Elevation
0 - 28 t
■ 1 285 - 568
I— 1569-853
■ 1854 -1 1 3 7
n
1138-1422
H 14^-1706
■
1707-1991
I— I 1992 - 2275
I— 12276 - 2560
Figure 2-2
The DEM data (with hill shade effect) in the study area.
28
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dry dipterocarps
Evergreen
Dry Evergreen
Pine Transition
Mixed deciduous
Evergreen Galleiy
Dry Dipterocarps
Buner Zone
Figure 2-3
Vertical distribution of forest types in the study area.
land cover type
B Tropical evergreen
10 5
H Mixed deciduous
Dt dipterocarps
] Agriculture/open
I Other
0
! Kilometers
Figure 2-4
Forest type map in the study area.
29
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Mae Chaem
Mae Samoeng
Mae Wang
.Mae Klang
Sites
GCP points
Figure 2-5
Study sites and ground control points (GCPs) in the study area.
(a)
Figure 2-6
(b)
fisheye picture and its transformation in angular coordinates.
30
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H
<
>
LU
_J
LU
LU
O
<
I
I
LU
LU
o;
I-
X
m
Q
X
LU
Q
LU
LU
QL
1X
I
>
Xo
X
<
o
g
CD
ELEVATION
Figure 2-7
AGE
TREE H
DBH CM
TREE DEN
CANOPY H
BIOMASS
Correlation scatterplot matrix of ground-measured data.
31
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«
c
0)
0 .9
E
S. 0.7
3
(/)
(Q
Q> 0.5
■ tree H (m)
■o
0>
yN 0.3
(Q
□ tree density (#/tia)
■ stem H (m)
EDDBH (cm)
E
□ biomass (ton/ha)
E
o 0.1
c
dry dipt mixed
decid
pine
trans
dry
ever
moist
ever
forest types
Figure 2-8
Normalized average ground measurements in different forest types.
(/>
4->
C
0)
E
0)
L.
3
■ tree H (m)
(0
■ stem H (m)
E
□ DBH (cm)
(A
O
XJ
0>
N
□ tree density (#/ha)
0 biomass (ton/ha)
75
E
k.
o
c
dry dipt mixed
decid
ever
forest types
Figure 2-9
Modified normalized average ground measurements in different forest
types (the outlier of dry evergreen site was removed).
32
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Chapter 3
Estimation of Tropical Forest Fractional Cover with Landsat
FTM+ and IKONOS Imagery
3.1
Introduction
Tropical forests are being subjected to various extents of disturbances in addition to
outright deforestation. Selective logging, forest fire, and disturbance increases along
newly created forest edges and result in significant changes in forest structure and canopy
integrity and often promote the probability of other types of disturbance. Degradation, in
the form o f decreased fractional cover, results from many direct and indirect human
impact including selective logging (Barros and Uhl 1995), forest fires (Cochrane et al.
1999), and biomass collapse in fragmented forests (Laurance et al. 1997). Forest cover is
an important indicator of forest degradation and forest thinning activities such as
selective logging and natural fire. An accurate assessment of the spatial extent of forest
cover is a crucial requirement for quantifying the sources and sinks of carbon from the
terrestrial biosphere (DeFries et al. 2000).
Forest fractional cover (fc) is the area covered by forest canopies per unit area on the
ground. At a larger or regional scale, it is also called forest canopy cover when the intra­
tree gaps are neglected. Field measurements generally provide good estimates of forest
fractional cover, but are often too expensive and time-consuming to be applied over large
33
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areas. Although direct measurement offc is theoretically possible with high spatial
resolution imagery, it is very expensive and sometimes impossible to cover large areas.
In general, natural tropical forests have dense fractional cover. In degraded areas, land
cover can be categorized into two types: trees and open areas. In the dry season, trees in
tropical forests can be characterized as green vegetation while open areas are often
senescent grass that has a similar spectral response to bare soil. Green vegetation has high
reflectance in the near infrared (NIR) and low reflectance in the red spectral regions,
while soil reflectance changes gradually with spectral wavelength. Therefore, vegetation
and bare soil have distinct spectral signatures that are separable in remote sensing images.
In many cases, however, a single pixel from satellite images does not contain pure
vegetation or open area. Instead, it often consists of a mixture of open area and
vegetation, especially in images with medium to coarse resolution. Therefore, the spectral
response of a pixel is a collective contribution from both green vegetation and the open
area. The spectral signature varies with the abundance of green vegetation. The more
green vegetation in a pixel, the higher the spectral response in NIR band and the lower in
red band.
Using data at medium to coarse resolutions, some effort has been made to estimate fc
with sub-pixel information in the reflectance domain (Quarmby, et al. 1992; Settle and
Drake 1993). Hanan and Prince (1991) combined red and near infrared reflectance in an
area-additive model to estimate the abundance of vegetation. Price (1992) estimated fc
and vegetation amount from visible and near infrared reflectance with AVHRR
34
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observations in a “mixed pixel” case. Jasinski and Eagleson (1990) used a pixel’s
distance to the soil line in the near infrared-red reflectance scattergram to estimate fc.
Radeloff et al. (1999) created fraction images in Jack Pine forests with spectral mixture
analysis of near infrared reflectance to detect hudworm defoliation. Townshend (1999)
and DeFries et al. (2000) derived global continuous vegetation cover with coarse
resolution AVHRR (1km) and MODIS (250m, 500m, and 1km) data.
Among the studies above, linear spectral mixture analysis, or spectral unmixing theory,
was used to compute the abundance or percentage of vegetation cover in one pixel
(Wessman et al. 1997; Adams et al. 1993). These models operated in the spectral
reflectance domain. However, the use of reflectance values with linear unmixing models
is problematic. Although any reflectance band could be applied in the model, it is
difficult to choose the optimal one. Most studies applied the red or near infrared bands.
Although the surface reflectance in these two bands is primarily a function of the
dominance of vegetation, it is also influenced by moisture and the structure of both
vegetation and soil and other external factors such as atmosphere conditions and suntarget-sensor geometry. When estimating the proportions of land cover within a pixel
with a linear mixture model in the reflectance domain, the standard error of estimates
between the percentages derived from mixture modeling and the actual land cover
percentages was as high as 11% (Townshend et al. 2000).
Vegetation indices (Vis), which are often mathematical combinations of different spectral
reflectances, suppress the spectral variations of reflectance, and make vegetation and
35
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non-vegetation comparisons more stable (Jasinski, 1990). In the past few years, more and
more attention has been made to estimate fc with the normalized difference vegetation
index (NDVT), one of the most commonly used vegetation indices. For example, Gutman
and Ignatov (1998) estimated regional green vegetation fraction distribution from
NOAA/AVHRR data using N D V Iin a linear unmixing model. Zeng et al. (2000)
validated this method for each land cover type in the International Geosphere-Biosphere
Program (IGBP) land cover classification scheme and created global 1-km fractional
vegetation cover maps with AVHRR data. Qi et al. (2000) reported that when using a
linear unmixing model with NDVI instead of spectral reflectance, the atmospheric effect
was reduced. They also built vegetation fc maps in a semi-arid rangeland with 1-km
SPOT VEGETATION, 30-m Landsat TM, and 3-m airborne TMS data. The results from
different datasets were highly comparable.
These studies assumed that a linear relationship between NDVI and canopy fractional
cover is adequate (Zeng et al. 2000; Wittich 1997; Gutman and Ignatov 1998). The
validity of the linear/c-NDVI relationship has been confirmed in some studies (Myneni
et al. 1992; Carlson et al. 1990; Wittich and Hansing 1995). However, some other studies
(Myneni and Williams 1994; Carlson and Ripley 1997) reported that the relationship was
non-linear because NDVI was rapidly saturated when vegetation amount increased.
In this chapter, a linear unmixing model in the vegetation index domain was applied to
estimate canopy fractional cover in tropical forests within the study area. Six commonly
used vegetation indices were calculated and the one that was most linearly related with
36
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vegetation amount was selected in the model. The forest fractional cover in the study area
was then estimated with an ETM+ image acquired in February 2000. The results were
validated with both ground measurement and 1-m pan-sharpened IKONOS data. The
seasonal variation of forest fractional cover distribution was also discussed and the
adjustment of the fractional cover map to wet-season conditions was examined.
3.2
Remotely Sensed Data
3.2.1
Landsat ETM+ image
A subset o f ETM+ data (30-m resolution) was processed to estimate forest fractional
cover in the Mae Chaem Watershed (Figure 3-la). The image was acquired on February
2, 2000 during the dry season. The deciduous forests, such as dry dipterocarps and some
mixed deciduous species, were senescent and leaf-drop had begun. The leaves of
evergreen trees, however, did not have significant annual change. A small area of
evergreen forests was covered by cloud and was replaced by an ETM+ scene acquired
one month later (March 5, 2000).
The ETM+ image in this study was atmospherically, geometrically, and topographically
corrected before further processing.
Atmospheric Correction
The ETM+ image was radiometrically corrected using the information in its header file,
the ETM+ image was converted from digital number (DN) value to top-of-atmosphere
(TOA) radiance and then TOA reflectance in the range of [0,1]. To represent the true
37
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surface conditions, the atmosphere effects need to be reduced so that the TOA reflectance
were converted to surface reflectance.
The ETM+ image was acquired in the early spring which is part of the dry monsoon
season. The atmosphere model was set as a typical Tropical Atmosphere, and the aerosol
model was Rural Extinction with visibility distance = 23km, a typical visibility in clear
air. The MODTRAN4.0 model was run to correct the atmosphere effect in the imagery.
The correction equations for bandl-5 were:
—0.114
P s u r fX ~
A«r/2 = l-3838pro^2-0-065
A„.^3=1.3192p,„,3 -0.0373
(3-1)
A«./4 = 1-353 lp ro ^ 4- 0.0201
Pw/5 =1-241
Here the
- 0.0035
is the surface reflectance, and
is the reflectance on the top of
atmosphere. After atmospheric correction, the DN values were converted to surface
reflectance in the range of [0,1].
Geometric correction
According to the information in its header file, the ETM-i- image was georeferenced to the
UTM projection, zone 47, spheroid and datum WGS84. However, due to system
distortions and the limited accuracy of the ground control, there was some deviation
between the UTM position in the image and its true position on the ground. In the areas
far from the image center, the deviation could be as much as several hundred meters.
Therefore, the georeferenced ETM-f image needed additional geometric correction.
38
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The ground GPS recordings of around 60 positions were collected during field trips.
These ground control points (GCPs) were associated with unique ground features, such as
the curve of a road, the center of a bridge, and the comer of a reservoir. It was easy to
recognize these positions in the ETM+ image. With these GCPs, a second-order
polynomial model was selected to do the geometric correction. The model had a total
error of 23.6m, less than one pixel of the ETM-^ image (30m).
Topographic correction
Mae Chaem Watershed is located in a mountainous area. Its elevation changes from
250m near Chiang Mai city to 2,550m at the peak of Mount Doi Inthanon. Slopes vary
from 0° to more than 50°. The topographic change in the study area makes the sunground-satellite geometry complicated and the surface reflectance varies with its local
incidence angle. Therefore, the topographic effect in the surface reflectance has to he
correeted before further processing.
In this study, a bidirectional reflectance distribution function (BRDF) model (Rahman et
al. 1993) was applied to correct the topographic effect in the ETM+ surface reflectance
image. For each pixel, the local slope, azimuth aspect, and local incidence angle were
calculated from DEM data (Figure 3-2a). A nadir view was chosen as the standard view
direction, and the reflectance from its local incidence angle was adjusted to the one with
standard incidence angle (0°).
39
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Let 6^, 0^, (p^, (p^ be sun zenith, sun azimuth, and satelliteview zenith, view azimuth
angles which can be found in the image header file {cp^ =0° and (p^can be any value for
ETM+ image), and a ,
the slope and aspect which can be calculated from DEM
data, then local sun zenith 6\ , local view zenith 9\,, and local relative azimuth angle cp
can be calculated:
cos O] = cos a cos
+ sin a sin 9^ cos(/? -(p^)
cos 9\^ = cos a cos 9^ + sin a sin 9^ cos(/3 -cp^)
9
(3-2)
= \P-(Ps\
Assuming the sun-satellite geometry
9^, 9^,
and
( p (relative
azimuth angle) as standard
geometry, the surface reflectance in this geometry is modeled as (Rahman et al., 1993):
PyPs’^ v ’9 )
where
(
a
n
(cos9^+cos9^)
r
9
13/2 '
[1 + 0 ' -2 0 co s(;r-< ^)J
1
I
l + <^
^
(3 3)
and k are two empirical surface parameters. 0 is the function parameter
controlling the relative amount of forward (0 < 0 < 1) and backward scattering (-1 < 0 <
0). The phase angle ^ and geometric factor G can be calculated by
c o s ^ = C O S 0 J c o s ^ ^ -I- s i n ^ j s i n 0 ^ c o s ^ ;
G = -sjtan^ 9^ + tan^ 9^ - 2tan0^ tan^^ cos(p .
Similarly, the surface reflectance p {9[,9[,(p ) in local geometry canbe calculated. The
correction coefficient p{9^,9^,(p)lp iG[,9\,,(p ) was then applied to theETM+surface
reflectance image.
40
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The topographically corrected image was much smoother even in areas with large
topographic variation. Figure 3-2b showed a subset of the ETM+ image (Band4+3+2)
near the peak of Mount Doi Inthanon before and after BRDF correction. It is obvious that
before BRDF correction, the topographic effect was dominant creating dark shadows in
the image. After correction however, the image was totally flattened and the shadows
were greatly reduced.
After atmospheric, geometric, and topographic correction, the ETM+ image was ready
for forest fractional cover estimation.
3.2.2
High-resolution IKONOS Images
For the purposes of spatial comparison and seasonal adjustment, ten DCONOS images
were acquired in the study area (Figure 3-lb), covering a total area of 673 km^ in Mae
Chaem Watershed. The study sites visited during the two field trips were also covered by
these IKONOS images. One IKONOS scene was acquired on February 27, 2000, 25 days
later than the ETM+ image acquisition, during the dry season. The remaining IKONOS
scenes were acquired on October 9, 2002, during the wet season. All of these IKONOS
images were geometrically corrected to the ETM-^ image. No atmospheric and
topographic correction was done on these IKONOS images because only classification
and visual interpretation were needed for the process.
The IKONOS images have four spectral bands at 4-m resolution and one panchromatic
band at I-m resolution. For each IKONOS image, the 1-m panchromatic band was
41
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merged with the 4-m spectral bands to produce a pan-sharpened multi-spectral image at
1-m resolution. Since the size of tropical trees is much larger than 1 meter, the pan­
sharpened IKONOS image reveals enough details for forest fractional cover calculation.
The pan-sharpened IKONOS images were then processed to calculate fc at a spatial imit
o f 30x30 m, comparable with the ETM+ estimated fc. The IKONOS calculated f c values
were assumed as “ground truth” to validate the ETM+ estimation in continuous spatial
areas.
Since most o f the IKONOS imagery was acquired in the wet season, the ETM+ estimated
fc from the dry season could be adjusted to the wet season conditions when the vegetation
was flourishing. After adjustment, the seasonal variation offc in deciduous forests was
minimized and, therefore, the forest fractional cover map provided a better description of
forest integrity in the study area.
3.3
Methods
3.3.1
A linear unmixing model
For a surface with multiple components, the macroscopic linear mixing model used to
estimate the spectral response can be expressed as:
[d ]=[J(I c ]
(3-4)
where [D] is the data matrix, [R] is the response matrix, and [C] is the eigenvector matrix
consisting o f the relative contributions of different components. It can also be written as:
(3-5)
7=1
42
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where di^k is the measured response of pixel k in wavelength i, n is the total number of
independent reflecting components in this pixel, r^ J is the response of component j in
wavelength i, and Cj ^ is the relative contribution of component j in pixel k.
Since the ETM+ image in this study was acquired in the dry season, the open areas in the
forests were bare or covered by senescent grasses. Consequently, each pixel of the ETM+
image can be assumed to consist of two components: green tree canopy and open area.
The spectral response of open areas is characterized by hare soil and does not change
much all over the study area. The senescent grass has a similar spectral response to bare
soil and, therefore, is neglected. The reflected spectral values of these two components
are independent of each other. Let R be the total spectral response of the pixel at certain
spectral band,
the response of tree canopy, and R^^^^ the response of open area,
Eq.3-5 becomes:
R = KanoJc + R ,^ ,S ^ -fc ) + 8
(3-6)
where fc is the forest fractional cover in the area corresponding to one pixel. An error
term e is introduced to account for some insignificant remaining components within the
pixel. In tropical forests during the dry season, the two components (green tree canopy
and open area) are dominant and the error term, 8, is ignored.
Eq.3-6 suggests that at any wavelength, the total reflectance of a mixed pixel is the linear
combination o f the reflectance from its vegetation and open area, weighted by their
percentage cover fc and (l-/c). With Eq.3-6, when R^„„„py and R^^^^ are known in an
43
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ETM+ image, the/c value can be easily calculated. However, the reflectance of surface
targets changes greatly at different wavelengths. For example, the reflectance of green
vegetation is high in NIR, but low in red wavelengths. Even at certain wavelengths, the
values o f R^„„„py and
are highly influenced by the vegetation wetness, structure, soil
moisture, and texture (Jasinski, 1990). Therefore, it is difficult to choose an optimal
wavelength for
and R^^^„ in Eq.3-6.
A vegetation index (ET) is a mathematical combination of multiple spectral bands, which
can suppress the external effects mentioned above (Gutman et al. 1998; Qi et al. 2000).
Replacing the spectral response R with VI, Eq.3-5 becomes:
VI = VI^^„^pyfc + VI,p,„{\-fc)
where
(3-7)
is the vegetation index of green tree canopies in one pixel, and
is the
vegetation index of open area in that pixel.
It should be noted, however, that although both vegetation index and spectral reflectance
are descriptions of vegetation properties, they are not linearly related. Eq.3-7 is not
directly deducted from Eq.3-6. Instead, it is an approximation by choosing the optimal VI
that is most linearly related to the vegetation amount in the study area.
3.3.2
Optimal vegetation index (FT)
Several vegetation indices (Vis) have been developed over the past decades. The most
commonly applied Vis include the Normalized Difference Vegetation Index (NDVI)
(Tucker et al. 1979); the Soil Adjusted Vegetation Index (SAVI) (Huete et al. 1988); the
44
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Modified Soil Adjusted Vegetation Index (MSAVI) (Qi et al. 1994); and some vegetation
indices for global applications such as the Enhanced Vegetation Index (EVT) (Huete et al.
1999); the Global Environmental Monitoring Index (GEMT) (Pinty and Verstraete 1992);
and MERIS Global Vegetation Index (MGVT) (Gobron et al. 1999). Among these
vegetation indices, NDVI is the earliest and the most widely applied vegetation index in
remote sensing applications. Most of other Vis are modifications NDVI in an attempt to
depress the influence of atmosphere and surface conditions and to improve its accuracy in
extracting vegetation information from remotely sensed data.
To select the optimal V Iin the linear unmixing model, all these Vis {NDVI, SA VI, MSA VI,
EVI, GEMI, and MGVI) were calculated using simulated surface reflectance in the
Scattering by Arbitrarily Inclined Leaves (SAIL) model (Verhoef 1984). The reflectance
was simulated as a function of leaf area index (LAI), an indicator o f green vegetation
abundance. A forest o f sparse to moderate density was modeled with LAI values from 0
to 4.0 at an interval of 0.2, which is similar to most of the forests in the study area. The
conditions o f dense evergreen forests were not considered because both Vis and forest fc
values became saturated when ZA7 was very high. The same sun-target-sensor geometry
as the Landsat satellite was used in the reflectance simulations.
As shown in Figure 3-3, the values of all six Vis increase with leaf area index. The EVI,
SA VI, and GEMI quickly reach their saturation zone when LAI approaches 3.0. Although
NDVI has a higher value and increases almost linearly at lower ZA7, it saturates at a
similar 7,A7 threshold as the other three vegetation indices. MGVI has highest value and
45
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does not saturate until LAI =4.0. But, it increases rapidly when LAI is low, then quickly
slows down when LAI is higher than 2.0. The MGVI-LAI curve is polynomial. MSA VI has
a more linear relationship with LAI. It saturates only when vegetation is very dense {LAI
higher than 4.0). Since we are interested in an index that is most suitable for tropical
forests, MSA VI was chosen as the optimal vegetation index in this study because of its
sensitivity at higher forest densities.
The MSA VI reduces the effect of the soil background by using a soil adjustment function
that is determined from local soil reflectance. Unlike the empirical factors in other Vis,
the adjustment factor in the MSAVI changes as a function of canopy density and the slope
of the soil line (Qi et al. 1994):
MSAVI ^ P m R
— (1 + 4 )
P red
(3-8)
-^ 0
where L^ is a soil adjustment function. It is expressed as a function of the soil line slope
and the reflectance properties on the ground:
4 = [(P™ - Pred ) Xslope +1 +
\ - 8.0 Xslope x {p^^^ - p^^^ )
(3-9)
In our study area, the slope of the soil line is 1.2, calculated with the surface reflectance
values of soil surfaces in the atmospherically corrected ETM+ data.
In the MSAVI image (Figure 3-4), the tropical evergreen forests have high values (light
gray to white). The clear-cut areas, bare soil, and fallowed agricultural fields have low
values (dark gray or black), while the dry dipterocarps and mixed deciduous forests are in
between.
46
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From Eq.3-7,/c can be expressed in the form of the MSA VI:
M SA V I-M SA V I^^.
m sa v i, „ ^ - m sa v i,^.
Here M S A V I and M S A V I are the endmembers in the linear unmixing model.
MSAVI
is the vegetation index of full-cover green forests with fc = 1.0. MSAVf^^„
is the vegetation index o f open area with fc = 0. The grasses and shrubs in open areas are
senescent in the dry season and, therefore, MSA Vf^^^ is close to the MSA VI value of hare
soil.
The two endmembers MSAVf^^^^y and MSAVfp^„ in Eq.3-10 can be identified in the
ETM+ image. A sand mining spot with a smooth light gray tone in the middle left of the
image was chosen to represent the open area in the study area. It had been observed from
the field trips that the top of Mount Don Inthanon was covered by the tropical moist
evergreen forests with very high fractional cover values. A small area with a smooth
bright red tone in the middle of the ETM+ image (band 4+3+2) was chosen to represent
full-cover green canopy. The mean of the MSA VI values in each area was calculated, and
the two endmembers in the linear unmixing model were thus determined: MSA
0.71, and MSAVf^^^ = 0.16. These values were used in Eq.3-10 to compute the forest
fractional cover in the study area.
47
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=
3.4
Canopy Fractional Cover Analysis
Figure 3-5 is the fc map estimated from the ETM+ image in the study area, binned into 10
groups with an interval of 10%. All pixels with fc value less than 0 or higher than 100%
(reaching saturation) were truncated into 0 or 100%, respectively. Non-forest surfaces,
such as villages, water bodies, fallowed agriculture fields, and clear-cuts, have a
fractional cover lower than 20%. Forests in the watershed, with fractional covers ranging
from 20% to 100%, are assigned a gradual color scheme changing from light grayish
green to dark green, indicating the increasing forest density.
The forest fractional cover map in Figure 3-5 shows a similar pattern with the land cover
and forest type map (Figure 1-4). Along with the altitude, the forest types change in a
sequence o f dry dipterocarps, mixed deciduous, dry evergreen and moist evergreen. In
accordance, the fractional cover changes from low values in dry dipterocarps to
saturation (100%) in moist evergreen forests.
Even at each forest type, the fractional cover changes greatly. The fractional cover for
each forest type is also not smoothly distributed. Rather, it is scattered because of various
disturbances. Since the ETM+ image was acquired during the dry season, dry
dipterocarps, especially at lower elevations, were mostly senescent or leaf-off. The
vegetation index values are thus much lower than other forests and, therefore, the
fractional cover is lower. Some dry dipterocarp forests even have fractional cover lower
than 20%. Aside from the seasonal effect, dry dipterocarp forests suffer from intense
human disturbance of burning for agriculture or cutting for firewood (personal
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communication with local foresters). Selective logging for valuable trees such as teak
also used to be a common human activity in the past decades. As a result, most of the dry
dipterocarp forests are young second-growth forests with a fractional cover ranges from
10% to 40%. The fc values of the forests are lower in areas close to villages and higher in
the mountains.
The mixed deciduous forests are found at higher elevations and are less affected by
seasonal variation and human activities. Clear-cuts for small area agricultural fields and
natural fires are thus the common types of deforestation. The fractional cover of mixed
deciduous forests ranges from 40% to 80%.
Evergreen forests occupy the highest elevations in the watershed. Human disturbance is
much lower than for other forests. Natural fire is thus the major disturbance in these
forests. Also, as the result of government policy decisions, some large areas, sometimes
the whole slopes, were cleared long time ago and evergreen forests regenerated slowly.
The fractional cover of the evergreen forests ranges from 70% to 100%. The fractional
cover values o f most moist evergreen forests on the top of the mountains are saturated.
3.5
Validation
The forest fractional cover estimated with the ETM-i- image was validated with both
ground measurements and high-resolution IKONOS images. The IKONOS images
acquired in the wet season can also be processed to evaluate the seasonal change of the
forest fractional cover distribution in the study area.
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3.5.1
Validation with ground measurements
The ground-measured fc values were assumed to be correct (i.e. ground truth). At each
site, ten fisheye pictures were taken along a 150-m transect (300-m in second field trip)
which was assumed to represent the area of 150x150m (or 300x300m). The fc values
calculated with these pictures were averaged to represent the fc in an area of 150x150 m
(300x300 m in second field trip). The/c estimated from ETM+ image at each site was
averaged using a 5x5 window (150x150 m) to match the area of the ground
measurements and to reduce the autocorrelations between pixels. Figure 3-6 compares the
ground-measured and ETM+ estimated fc values. To be seasonally matched, only
measurements from the dry season were compared.
Figure 3-6a shows that the ETM+ estimated fc is correlated with ground measurements
(R^ = 0.757). The regression line deviates from the 1:1 line, indicating that the ETM+
estimated fc is different from ground-measured fc. In sparse forests where the fractional
cover is low, the ETM+ estimation is lower than the ground measurements. Contrarily,
the ETM+ estimation is higher than ground measurement in dense forests. The ETM-+estimation matches well with ground measurements in forests with moderately high
density, in which the fc is around 70% - 85%. In very dense forests, the ETM+fc
estim ation saturates w h ile the ground m easurem ents are less than 95% .
The difference is also shown in Figure 3-6b. Both the ETM+ estimated and groundmeasured fc values increases along the elevation gradient, in accordance with the forest
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types changing in a sequence of Dry dipterocarps, Mixed deciduous, Pine transition. Dry
evergreen, and Moist evergreen. For Dry dipterocarps, the ETM+ estimated fc is much
lower than ground measurement. For Mixed deciduous and Pine transition forests, the
ETM+ estimated fc is lower than the ground measurements, but the difference is much
less than that of Dry dipterocarps. For evergreen forests, the ETM+ estimated fc is a little
higher than the ground measurements. The ETM+ estimated fc is saturated in the moist
evergreen forests (reaching 100%).
The difference between the ETM+ estimated and ground-measured fc comes from the
different processing mechanisms. The ETM+ estimated fc in each pixel is calculated with
a linear unmixing model in which the vegetation index is a combined contribution of
green canopy and open area. Only green leaves in forests are considered in the model.
Therefore, the ETM+ estimated fc is actually the “green” fractional cover. The groundmeasured fc, however, is calculated from a binary classification of a hemispherical
photograph taken on the ground. All elements except open sky, including green leaves,
senescent leaves, stems, branches, and trunks, contribute to the value. In this meaning,
the ground estimated fc is the total cover of forest projected area.
The measurements shown in Figure 3-6 were made in the dry season when deciduous
forests are senescent or leaf-off. Therefore, in dry dipterocarps, the ETM-i- estimated fc is
low due to a lack of green leaves. The ground-measured fc, however, is much higher
because o f the significant contribution from the woody components (Figure 3-6b). In the
mixed deciduous and pine transition forests, some species were partially leaf-off and
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others remained evergreen. Consequently, both the ETM+ estimated and groundmeasured fc values are higher. The ETM+ estimated fc is still lower than the ground
measurements, but the difference is much smaller. Moreover, the vegetation index of pine
trees is generally lower than that of broadleaf trees due to the form of the needle leaves.
This also makes the ETM+ estimated fc in the pine transition zones lower than the ground
measurements. Both the dry evergreen and moist evergreen forests are much denser than
the other forest types of the area. The leaves remain green in the dry season and,
therefore, both the ETM+ estimated and the ground-measured fc values are high (>90%).
In dense evergreen forests, the in-tree gaps become dominant when calculating/c with
circular hemispherical photographs using the GLA software. However, these small-size
gaps are unrecognizable in the ETM+ image with 30-m resolution. As a result, the ETM+
estimated fc is higher than the ground measurements. In very dense, moist evergreen
forests, the ETM+ estimated fc reaches saturation while the ground measurement is less
than 95%.
Figure 3-6 explains the differences between ETM+ estimated and ground-measured fc
values in each forest type. For fractional cover distribution in a large area with all forest
types. Figure 3-6a shows that the ETM+ estimated fc values are highly related to the
ground truth. This relationship indicates that the forest fractional cover distribution in the
study area can be estimated with ETM-i- image with a reasonably high accuracy,
especially given the leaf-off issue. This validation, however, was based on ground
measurements in only 32 isolated sites. To make a spatial validation in the study area, the
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high resolution IKONOS images were assumed as ground truth to compare with the
ETM+ estimated fc distribution.
3.5.2
Validation with 1-m IKONOS data
The pan-sharpened DCONOS images have four spectral bands at 1-m resolution in which
the openings between the trees are visible. In this study, the fc derived from the IKONOS
data was assumed to be correct (i.e. ground truth) wherever intensive ground
measurements are not available. Each IKONOS image was processed in six steps to
calculate fc in a unit area of 30x30 m:
Step 1: Unsupervised classification
Based on the gray values in the four spectral bands, the pixels in the IKONOS image
were grouped into 50 clusters using the ISODATA unsupervised classification technique.
A maximum iteration of 50 in 95% convergence level was applied in the classification.
The signature file was thus created for next step.
Step 2: Supervised classification
With the experience o f the field trips, the 50 signatures in the signature file were merged
into several signatures based on the land cover types and seasonal variation. With the
new signature file, a maximum likelihood supervised classification was made of the
IKONOS image and a land cover image was generated. There were 5 classes in the dry
season: forest, shaded forest, bare soil, shaded bare soil, and water body. For the wet
season, there were 8 classes: forest, shaded/dark forest, bright agriculture/open area,
fallowed agriculture/open area, bare soil, shaded bare soil, water body, and cloud/shadow
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(if any). Figure 3-7 shows the feature spaces of each cluster hetween hand 3 (red) and 4
(NIR). The bright forest and dark forest are combined into one cluster because they are
the major concern in this classification procedure. All classes are very separable.
Step 3: Maioritv filtering
In the land cover image created in step 2, there were often some isolated classes
embedded in large classes. For example, there were agriculture fields with one or two
pixels in forests. These pixels were not real classes and should be removed. Using a 3x3
moving window and a majority decision rule, the pixels in the small classes were merged
into the larger surrounding classes.
Step 4: Forest/non-forest class
The land cover image was recoded into a 0/1 binary image, assigning forest and shaded
(or dark) forest as 1 and all others as 0. The resulting forest/non-forest image maintained
a pixel size of I meter.
Step 5: 30x30 matrix convolution
The forest/non-forest image was convolved with a 30x30 moving window to match the
pixel size of the ETM-i- image. The resultant pixel value was a float point value between
0 and I, which was equal to the forest fractional cover in a 30x30m area on the ground.
Step 6: Forest fractional cover
Applying the nearest neighbor technique, the image from step 5 was resampled to 30-m
pixel size. The result was the IKONOS calculated fractional cover distribution with a unit
area of 30x30 m.
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The IKONOS estimated fc distribution in the study area is an alternative to “ground
measurements” to spatially validate the ETM+ estimation. It should be noted that, the
IKONOS/c values were calculated with unsupervised and supervised classification
techniques and were very different from both hemispherical photographs for ground fc
calculation and the linear unmixing model for ETM+ fc estimation.
The ground-measured and the IKONOS and ETM+ estimated fc values were compared in
Figure 3-8. To avoid the seasonal effect, only fc data in the dry season were compared.
Only 7 study sites were covered by the IKONOS image in the dry season. All forest types
were included: dry dipterocarps (1), mixed deciduous (2), pine transition (1), and
evergreen (3). Among these 7 sites, only 6 sites were measured during the second field
trip. Therefore, in Figure 3-8a, there are 7 points of ETM+fc, 1 points of IKONOS fc,
and 6 points of ground-measured/c.
When compared with ground fc values from fisheye photos, the IKONOS fc was
underestimated in sparse forests and overestimated in dense forests (Figure 3-8a). This
could be explained by their different processing mechanisms. In dry dipterocarp forests
that have low fractional cover in the dry season, the light-colored stems/branches and
senescent leaves are more easily mis-classified as bare soil in an IKONOS image.
However, they are important non-sky components in the hemispherical photos. In dense
evergreen forests, the in-tree gaps less than 1x1 m were not observable in the IKONOS
image, whereas they played an important role in hemispherical photos. The small-area
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gaps in the IKONOS classification images were also smoothed out during the majority
filter processing.
These differences aside, Figure 3-8a shows that the IKONOS fc is highly correlated to
ground measurements (R^ =0.97). This indicates that where intensive groimd
measurements are lacking, the IKONOS fc could serve as ground truth for the purpose of
validation. The corresponding ETM+/c values at the limited study sites were also plotted
in Figure 3-8a. They too are highly correlated with the IKONOS fc (R^ =0.96). The
correlation line is close to the 1:1 line, indicating that the ETM+ image can be processed
to estimate/c with high accuracy.
Instead of the limited ground measurements, the IKONOS estimated fc values could be
compared with the ETM+ estimation in any corresponding areas. In this study, four
subset areas in different forest types were chosen for the comparison: dry dipterocarps,
mixed deciduous, transition zone, and evergreen forests. Since it is impossible to identify
pine transition forests in the ETM+ image, the transition zone covers mixed deciduous,
pine transition, and dry evergreen forests. The subset of evergreen forests covers both dry
evergreen and moist evergreen forests because it is difficult to identify them in the ETM+
image. The subset of evergreen forests was chosen nearby the peak of Mount Doi
Inthanon. In each subset, 100 positions were randomly selected to avoid the
autocorrelation effect. The fc value at each position was an average of a 3x3 window to
reduce the possibility of geometric mismatch between the ETM+ and IKONOS images.
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As shown in the scatterplot (Figure 3-8b), the accuracy of the ETM+ fc estimation varies
with different forest types. For dry dipterocarps that have a low forest density, the fc
values o f both the IKONOS and ETM+ estimation are more scattered than other forest
types. Most of the ETM+^c values are clustered in a range of 20% to 40% while the
IKONOS fc values ranges from 10% to 60%. The distribution of the ETM+fc in dry
dipterocarp forests is smoother than that of the IKONOS, but the values are
underestimated. The ETM+ estimation is better for the mixed deciduous forests whose
density is higher. However, the ETM+fc is still underestimated, ranging between 30%
and 60%, whereas the IKONOS fc could be as high as 80%. For the transition zone that
has a much higher forest density, the ETM+ and IKONOS fc match well. Both the ETM+
and IKONOS f c values range from 60% - 90%, and the data points are closer to the 1:1
line. For the moist evergreen forests where the density is very high, both ETM+ and
IKONOS estimated/c values are saturated.
All points in Figure 3-8b are more or less scattered along the 1; 1 line. The ETM+fc is
correlated with the IKONOS fc (R^= 0.70). Similar to Figure 3-6a, it indicates that the
ETM+ image is a good source of forest fractional cover estimation over large areas.
Figure 3-8b also shows that when forest cover is not saturated, the higher the forest
density, the higher the accuracy of the fc estimation.
Figure 3-9 presents the IKONOS image, the corresponding subset of the ETM+ image
and their fc maps. To avoid the seasonal variation, only the IKONOS image acquired in
the dry season (February 27, 2000) was compared. The IKONOS and ETM+ estimated fc
distributions show both similarities and differences in each forest type. In the fallowed
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agriculture fields in the lower left of the subset, the IKONOS estimated fc values are
around 0, and the ETM+ estimated fc values are less than 20%. In dry dipterocarp forests
in the upper left of the subset, both the IKONOS fc and ETM+fc have a wide range of
distribution. However, the IKONOS fc is more scattered with a range of 0-60%, while the
ET1V1+fc has a smaller range of 20-40%. Conversely, in the evergreen forests in the right
of the subset, the IKONOS fc distribution is less variable than that of the ETM+. In the
mixed deciduous forests in the middle of the subscene, the fc distributions from the two
images are very similar.
The secondary forests that are visually interpreted in both images can also be identified in
the^c maps. In the middle of the subset, there is large area of regeneration of evergreen
forests in their early stage. Both ETM+ and IKONOS fc values are in a range of 20-60%,
much lower than the natural, mature evergreen forests nearby. In the late-stage secondary
forests such as the isolated small areas in the lower right of the subset, the ETM +/c map
has fractional cover values around 60-80%. However, in the IKONOS fc map, the values
exceed 90% and cannot be separated from the dense, mature evergreen forest nearby.
The differences between the IKONOS and ETM+ estimated fc values are mostly at local
scales. When all forest types are considered in the whole subset, the ETM+ estimated fc
distribution shows high similarity with the one from the IKONOS image. In Figure 3-9,
from west to east o f the subset, the forest types change from dry dipterocarps, mixed
deciduous, and evergreen forests as visually interpreted in the ETM-i- image. The fc
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values in both the IKONOS and ETM+fc maps also show a gradual increase from 0 to
100%.
As seen in Figure 3-8 and 3-9, the accuracy of ETM-f- fc estimation is high when
compared with the IKONOS fc that is presumed correct. The accuracy also varies with
forest types in different fractional cover densities. For dry dipterocarps that have low
fractional cover in the dry season, the fc is underestimated with the ETM+ image,
although its distribution is less variable than the IKONOS fc. For mixed deciduous and
transition zone forests, the correlation between the ETM-H and IKONOS estimated fc
values are the highest among all forest types. For evergreen forests, especially moist
evergreen forests, both the ETM-^ and IKONOS estimated/c tends to saturate. The
ETM+ estimated fc map also reveals the different stages of forest clear-cut and regrowth
in evergreen forests. In general, the higher the forest density, the higher the accuracy of
ETM +/c estimation. Most of the tropical forests have high density and, therefore, the
method presented in this study is promising for large-area fractional cover estimation,
tropical forest evaluation, and ecosystem management.
3.5.3
Seasonal adjustment of fractional cover
The green fractional cover in the wet season is a more realistic representation of the
fractional cover distribution when all types of forests are leaf-on. However, sky
conditions during the wet season in the tropics are often cloudy or hazy. For satellite
sensors with coarse temporal resolution and wide swath such as ETM-i- (16 days, 180km
swath width), it is often difficult to acquire clear images during the wet season. It is.
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however, possible for the IKONOS satellite who has a sensor with a more flexible repeat
period (an average of 1.5 days). The IKONOS images acquired in the wet season can be
used to adjust the ETM+fc from the dry season to the “true” green cover of the peak
growing season.
Nine IKONOS images were acquired on October 9*, 2002 (wet season). These images
covered 28 study sites, among which 7 sites were measured in August 2001 (wet season).
One IKONOS scene was acquired on February 27*, 2000 (dry season), covering 7 study
sites, among which 6 sites were measured in January 2002 (dry season). Figure 3-10
shows the relationships between the IKONOS and ETM+ fc in wet and dry seasons.
Three series of data points are displayed: IKONOS and ETM+fc values in the dry season
(7 points), IKONOS fc in the wet season, but ETM+fc in the dry season (28 points), and
ground-measured/c in the wet season, but ETM+fc in the dry season (12 points).
The 7 points of IKONOS vs. ETM +/c in the dry season are closely distributed along the
1:1 line, indicating again that the ETM-i- fc during the dry season is estimated with high
accuracy. The relationships of IKONOS + ground fc in the wet season vs. ETM+ fc in the
dry season, however, is not linear. Due to the seasonal variation o f forest greenness, the
fc in the wet season, especially in dry dipterocarps and mixed deciduous forests, is much
higher than the fc calculated during the dry season. The fc in the pine transition and
evergreen forests does not change much across the seasons. Both IKONOS and groundmeasured fc values in the wet season follow a similar trend and, therefore, can be
combined as ground truth.
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As shown in Figure 3-10, the ground truth (IKONOS and ground-measured fc) from the
wet season is logarithmically related to the ETM+fc calculated from the dry season. A
simple logarithmic regression was processed and the regression line is shown in Figure 310. With the obvious three outliers in the upper left of the figure, the expected value is
higher than the observed when fc is less than 50%. The squared correlation coefficient
(R^) of the regression is only 0.64. When the three outliers were removed, a
logarithmically curve fitting was also made with the following equation:
=0.30* ln (/c ^ ^ /100+ 0.09)+ 0.91
fc^e,=fodry
when
fc <91%
/c > 9 1 %
Here, fc^^^ is the expected fc in the wet season, and fc^^ is the observed fc from the
ETM+ image in the dry season. Using Eq.3-11, the expected fc^^^ is only 94% when
ETM+fc is saturated (100%), which is less than the ground truth. Therefore, fc^^, is
underestimated in very dense forests. Since there is not much difference between fc^^^
and fc^^ in dense evergreen forests, we set fc^^,
fc dry when the ETM+ estimated fc >
91%.
The curve fitting is demonstrated in Figure 3-11. The measured data points were closer to
the curve fitting line when the outliers were removed. The squared correlation coefficient
(R^) for this curve fitting was 0.87, much higher than the simple logarithmic regression in
Figure 3-10. The errors of the three coefficients in Eq.3-11 were much lower than the
expected values. A
goodness-of-fit was also done to this curve fitting and the
0.15. For a 3-parameter curve fitting with 37 samples (40 data points - 3 outliers), the
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degree o f freedom equals 34 (37 - 3 model coefficients). At 95% confidence level, the
standard
x I.qs,^
= 48.60. Since
much less than
x I.osm >we
accept the curve fitting
described by Eq.3-11.
With Eq.3-11, the ETM+ estimated/c in the dry season could be adjusted to the “true”
green forest fractional cover of the wet season, a more realistic biophysical attribute in
tropical forest ecosystems. Figure 3-12 is the adjusted fractional cover map. It is obvious
that the distribution is much less spatially variable than the one in the dry season (Figure
3-5). For dry dipterocarps and mixed deciduous forests, the green cover is much higher
than the cover calculated from the dry season. The fractional cover of evergreen forests
does not change much. Except for the non-forest areas like agriculture fields and
settlements whose cover is less than 20%, the green forest fractional cover in the
watershed ranges from 20% to 100%. The fractional cover of most forest areas in the
watershed is higher than 50% during the growing season.
3.6
Conclusion and Discussion
Forest fractional cover is a good indicator of forest integrity. Quantifying the fractional
cover in tropical forests is very important for evaluating deforestation and recovery. In
this study, a linear unmixing model in a vegetation index domain was built to estimate the
forest fractional cover distribution in the tropical forests of the Mae Chaem Watershed in
northern Thailand. The Modified Soil Adjusted Vegetation Index {MSA VI), which was
most linearly related with vegetation abundance, was chosen as the independent variable
in the model. Two endmembers, full-cover forest canopy and open area with bare soil or
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senescent grass, were calculated through statistical analysis. With the model and the
ETM+ image acquired in the dry season, the forest fractional cover distribution in the
watershed was mapped.
The forest fractional cover distribution in the study area results from the different extent
of both human disturbance and seasonal variation. Along the elevation gradient in the
mountainous watershed, the forest types change from dry dipterocarps at low elevation,
to mixed deciduous, pine transition, dry evergreen, and finally moist evergreen forests on
the top o f the Mount Doi Inthanon. The forest fractional cover increases along this
gradient (downhill to uphill) in a similar trend to the forest type change. The dry
dipterocarp forests are frequently burned for agriculture and regrow after the agriculture
fields are abandoned. Moreover, as deciduous forests, the dry dipterocarps vary
seasonally. During the dry season, the leaves of dry dipterocarp forests become brown
and fall off. Therefore, as estimated with the ETM+ image, the dry dipterocarps have a
lower fractional cover between 10-40% in the dry season. The mixed deciduous forests
are composed of both deciduous and evergreen species. Also, because of the relative
difficulty to access these types, the mixed deciduous forests are less disturbed by human
activities. As a result, their fractional cover is between 40-80% in the dry season. The
pine transition and evergreen forests are composed of only evergreen species. Similarly
inaccessible due to the high topography, they are not degraded much as the dry
dipterocarps and mixed deciduous forests. The fractional cover of these forests is
generally higher than 70% in the dry season, and the seasonal variation is minimal. For
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the moist evergreen forests on the top of Mount Doi Inthanon, the fractional cover is
often saturated (i.e. 100%).
In each forest type, the fc distribution reveals different stages of forest degradation and
recovery in the study area. For example, there were isolated areas of clear-cutting in the
evergreen forests in the early 1990s. The regrowth of these areas shows in the fractional
cover map with the cover varying between 20 to 80%, lower than the surrounding mature
evergreen forests.
The forest fractional cover estimated with the ETM+ image was validated by isolated
groimd measurements in the watershed. The ground-measured fc was calculated using the
GLA software to process hemispherical photographs taken on the ground with a fisheye
lens. The ETM+ fc values were correlated with those at the 32 study sites measured in the
dry season with
0.76. Similar to the trend of ETM+fc, the ground-measured fc
increased with elevation as the forest types changing from dry dipterocarps to mixed
deciduous, pine transition, and finally dry and moist evergreen forests. Due to the
methodological difference, the ETM+fc was lower than the ground-measured fc in lowdensity forests such as dry dipterocarps, and higher than ground-measured fc in dense
forests such as the tropical evergreen. The ETM+fc was saturated in moist evergreen
forests at high elevations.
The forest fractional cover estimated with the ETM-f image was further validated in a
large spatial area by one high-resolution IKONOS image that was also acquired in the dry
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season, 25 days after the acquisition of the ETM+ image. The IKONOS fc was calculated
by image classification and visual interpretation. With limited study sites covered by this
scene (7 sites only), the squared correlation coefficient between the ground-measured fc
and the IKONOS fc was 0.97, indicating the high probability of the IKONOS image
serving as an accurate validation source in this study. Also at these study sites, the ETM+
fc was highly correlated with the IKONOS fc with R^= 0.96. The correlation line was
very close to the 1:1 line in the scatterplot. The ETM+fc was also compared with the
IKONOS derived fc in a larger dataset, in which 100 points in each forest type were
randomly selected for the purpose of comparison (in total of 400 points). In the ETM+ vs.
IKONOS fc scatter plot, these 400 points were loosely clustered along the 1:1 line. The
squared correlation coefficient was 0.70.
The ETM+ fc estimated in the dry season can be adjusted to the “true” green cover with
ground measurements and IKONOS images from the wet season. A logarithmical
relationship was observed and a curve fitting model was built using the combined
ground-measured and IKONOS fc in the wet season and the ETM+ fc in the dry season.
With this relationship, the ETM+fc map in the dry season was adjusted to wet season
conditions, a better representation of tropical forest canopy cover.
The successful estimation of forest fractional cover is an obvious improvement over
simple forest/non-forest binary classifications. Knowing the fractional cover distribution
in tropical forests provides a better way to measure both forest degradation and its
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recovery. It is also very helpful for estimating forest biomass in tropical forests and
understanding their role in carbon sequestration and global change.
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3.7
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Frazer, G. W., Canham, C. D. and Lertzman, K. P. (1999). Gap Light Analyzer (GLA),
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transmission indices from true-color fisheye photographs, users manual and
program documentation. Copyright © 1999: Simon Fraser University, Burnaby,
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Environment: 25, 295-309.
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Huete, A.R., Justice, C. and Van Leeuwen, W. (1999). MODIS vegetation index, MODIS
algorithm theoretical basis document, NASA Goddard Space Flight Center,
Greenbelt.
Laurance, W. F., Laurance, S. G., Ferreira, L. V., Rankin-de Merona, J., Gascon, C. and
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Jasinski, M. F. (1990). Sensitivity of the normalized difference vegetation index to
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radiation absorbed and reflected by vegetated land surfaces. IEEE Transactions on
Geoscience and Remote Sensing: 30, 303-314.
Myneni, R. B. and Williams, D. L. (1994). On the relationship between FPAR and NDVL
Remote Sensing o f Environment: 49, 200-211.
Pinty, B. and Verstraete, M. M. (1992). GEMI: a non-linear index to monitor global
vegetation from satellites. Vegetatio: 101, 15-20.
Price, J.C. (1992). Estimating vegetation amount form visible and near infrared
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Qi, J., Cbebbouni, A., Huete, A. R. and Kerr, Y. (1994). A modified soil adjusted
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Qi, J., Marsett, R. C., Moran, M. S., Goodrich, D. C., Heilman, P., Kerr, Y. H., Dedieu,
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reflectance (CSAR) model, 2, semi-empirical surface model usable with NOAA
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advanced very high resolution radiometer data, Joumal of Geophysical Research:
98(20), 781-801.
Settle, J. J. and Drake, N. A. (1993). Linear mixing and estimation of ground cover
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Applied Meteorologv: 39(6), 826-839.
69
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M ae C h a em
C M aeS am oen
m
02/27/2000
M aeW an g
M ae Klan
(a)
Figure 3-1
(b)
ETM+(band4+3+2, 02/02/2000) (a) and IKONS images (band4-i-3+2,
02/27/2000 and 10/09/2002) (b) in Mae Chaem Watershed. The dots in (b) represent the
study sites in two field trips.
70
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Before
blevation
0-2S^
285-568
I— I 959854-1137
1138- 1422
1423- 1706
1707- 1991
r ~ l 1992 - 2275
I— I 2276 - 2503
(a)
Figure 3-2
(b)
DEM data with hillshade effect (a) and topographic correction with
Rahman’s BRDF model (b).
S
0.8
-GEMI
NDVl
SAVl
- MSAVI
-EVI
■MGVl
■a
c
C
o
b
O)
>
0.6
0.4
0.2
0.0
0
1
2
3
4
5
Leaf Area Index
Figure 3-3
Dynamic ranges of the six vegetation indices calculated with simulated
spectral data in SAIL model.
71
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Figure 3-4
MSAVI image in Mae Chaem Watershed.
.^1-10%
> • 60 %
0 • (KF
Figure 3-5
Forest fractional cover map in Mae Chaem Watershed.
72
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100
= 0.757
♦♦
80
£
60
o
nE
J
~E gM
(A N
0
)o
+
40
S
20
0
0
40
20
60
80
100
ground measured fc (%)
(01//20-27/2002)
(a)
100
El ground
■ ETT\/I+
dry dipter.
mixed
pine
decid.
trans.
dry ever.
moist
ever.
(b)
Figure 3-6
Comparison of ground-measured and ETM+ estimated fc: all forest types
combined (a) and by forest type (b).
73
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
200
150
c(Q 100
<4O
X
z
X
o forest(bright+dark)
Q
o bare
50
A bright open
+ dark open
X shaded bare
o water
0
0
50
100
150
200
DN of band 3 (Red)
Figure 3-7
Feature space of different classes in the supervised classification of
IKONOS images.
74
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fc ground Jan
Y = MO + M r x
32.38
0.60209
0.98309
fc ETM Feb
Y = MO + M1*X
0.37416
1.0101
0.98046
30
40
50
60
70
80
90
100
IKONOS fc in dry season (Feb)
(a)
100
o dry dipterocarps
X mixed deciduous
o transition zone
• evergreen
UJ
20
R"= 0.70
20
40
60
80
100
IKONOS fc (dry season) (%)
(b)
Figure 3-8
Scatter plots of ground-measured and IKONOS estimated fc in 6 sites (a),
and the ETM+ and IKONOS estimated fc in 400 randomly selected points (b).
75
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
ETM+ image (4+3+2), 02/02/00
IKONOS image (4+3+2), 02/27/00
0“o
1-10%
ii-m
21-30*,.
3MW-0
11-50%
5l-6 0 5 i
6|.70«-;>
TI-HO'-'o
81-m
0I-1(K)%
I
IKONOS estimated fc
Figure 3-9
ETM+ estimated fc
IKONOS and ETM+ subset images and their fc maps.
76
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100
40
Ofc_ground_wet
)KfcJkonos_wet
• fc_ikonos_dry
CD
0
20
40
60
80
100
ETM+ fc in dry season {%
The ground-measured, IKONOS and ETM-i- estimated fc scatterplot in dry
Figure 3-10
and the wet seasons.
1
c
o
03
0.8
CO
03
03
03
?
0.6
C
T3
C
3
O
0.4
y = m1 + m2 * ln(M0+m3)
OT
CO
o
z
O
14
ml
m2
m3
0.2
Chisq
0
0 .2
0 .4
Value
Error
0.907
0.298
0.086
0.145
0.934
0.017
0.049
0.053
NA
0 .6
NA
0 .8
ETM+ fc in dry season
Figure 3-11
The curve fitting model to adjust fc in the dry season to the wet season.
77
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61-70%
?l-80%
Figure 3-12
Forest fractional cover map adjusted from the dry season to the wet
season.
78
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Chapter 4
Estimation of Leaf Area Index with Fractional Cover Data in
Tropical Forests
4.1
Introduction
Leaf area index {LAI), the total single-sided leaf area per unit ground area, is another
important biophysical attribute of forest ecosystems. It controls the partitioning of
incoming solar energy into sensible and latent heat fluxes and is a good indicator of
energy, gas and water exchanges between land surface and atmosphere. Because of its
important rule in the physiological processes such as photosynthesis, transpiration and
evapotranspiration, LAI has been widely used to parameterize and validate models of
ecosystem functioning, biosphere-atmosphere interaction, vegetation growth, net primary
production and other environmental processes at landscape to global scales (Sellers and
Schimel 1993).
The direct measurement of LAI involves cutting and measuring the leaves in a small area.
It provides the most accurate measurements. However, this method is very time
consuming and destructive. As a result, this method is not recommended for long-term
large scale measurements. An altemative way is to indirectly measure LAIWiXh
commercial instmments like the Li-Cor LAI-2000 and fisheye photography (Welles
1990). The accuracy o f these indirect methods highly depends on the sunlight radiation
model that these instruments use and the atmosphere conditions at the time of
79
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measurement. Both direct and indirect
measurements provide ground data for the
validation of remote sensing methods at large scales.
One of the primary approaches to estimate LAI using remote sensing imagery is the
polynomial regression between LAI and vegetation indices (FIs) from radiometric
measurements (Qi et al. 2000; Baret 1995; Best and Harlan 1985; Peterson et al. 1987).
This relationship is non-linear (Tucker et al. 1981) and saturates at LAI > 3 depending on
the type of vegetation index used, the canopy type studied, and the experimental
conditions (Spanner et al. 1994; Baret and Guyot 1991). This empirical LAI-VI
relationship is also affected by leaf pigments, internal structure, orientation distribution,
leaf clumping, woody reflectance, and heterogeneity in tree height and tree gaps (Turner
etal. 1999).
Another commonly applied approach is to estimate LAI by the inversion of bi-directional
reflectance distribution function (BRDF) models (Pinty et al. 1990; Goel and Thompson
1984a,b; Qi et al. 1995). So far, more than a dozen BRDF models have been developed
for various types of surface such as cropland, grassland, and bare soil surfaces (Strahler
1994). Some of these models are mathematically invertible so that biophysical attributes
can be calculated. However, a BRDF model often has multiple input parameters. For
large-scale model inversion, some of the essential input forest parameters are often
unavailable and have to be simplified. This simplification often results in unreasonable
LAI values for forests with high heterogeneity of distribution and topography.
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Because of the topographical difficulty to access, it is impossible to measure
with
LAI-2000 instrument at study sites in the watershed. However, as discussed in Chapter 3,
the forest fractional cover was measured with a fisheye camera at each study site, and the
forest fractional cover product from remotely sensed data was validated. In this chapter, a
simple regression model was developed to retrieve forest LAI distribution in the study
area from forest fractional cover product in Chapter 2. The adjustment of Z.T/product
from the dry season to the wet season was also performed.
4.2
Experimental Design and Field Measurements
4.2.1
LAI-2000 and fisheye photographs
A variety of instruments have been developed which apply radiation models to indirectly
measure the vegetative canopy attributes. When passing through the canopy, solar
radiation is mainly alternated by foliage amount and orientation. The spatial distribution
o f the foliage is assumed to be random in the radiation model. The common strategy of
the radiation model is to describe how radiation is affected as it passes through a canopy
with some well-defined, geometrical canopy attributes, then make appropriate radiation
measurements and invert the model to estimate the value of these attributes (Welles and
Cohen 1996). The success of the radiation model is thus determined by how closely the
real canopy conforms to the idealized one.
It should be noted that, when passing through a canopy, the radiation is not only affected
by leaves, but any other opaque object like stems, fiiiits, and branches. As a consequence.
81
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although LAI is defined as leaf area index, the LAI estimates with indirect methods should
be better described as plant projected area index (Welles and Norman 1991).
The LAI-2000 Plant Canopy Analyzer developed by LI-COR company is the most
commonly used commercial instrument to measure leaf area index and some other
vegetative canopy attributes. It uses hemispherical optics and a ringed detector that
simultaneously measures diffuse radiation in five distinct angular bands about the zenith
(Welles and Cohen 1996). For a single-sensor LAI-2000 equipment, a reference reading
is made at the open area above the canopy, instantly followed by one or more below
canopy readings. Sometimes another open reading is made after the below canopy
readings. For each ring, the canopy’s gap fraction at the view angle of that ring is
assumed to be the ratio of the below and above canopy readings (Welles and Norman,
1991). With a sunlight radiative transfer model, the LAI, foliage amount density, and
foliage orientation can be calculated. The LAI-2000 has proven to be very useful in short
canopies like agricultural crops and grasses.
However, for tall canopies like forests, the application o f LAI-2000 is limited. For each
ZA/measurement, a simultaneous reference reading is needed in an open area that is
large enough to avoid the effects of forest scattering. In this study, all of the study sites
are located in the deep forests in Mae Chaem Watershed and, therefore, it was impossible
to measure
with the LAI-2000 instrument during the two field trips.
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As discussed in Chapter 3, the digital photos with fisheye lens were taken at the study
sites and processed with the Gap Light Analyzer (GLA) software to calculate the gap
fraction. A similar radiation model was also applied in GLA to calculate LAI over certain
zenith angles. For example, LAI-4ring is the effective leaf area index integrated over the
zenith angles 0° to 60°, and LAI-Sring is the effective leaf area index integrated over the
zenith angle 0° to 75° (Frazer et al. 1999). The forest fraction (in 0-1 scale) is 1.0 - gap
fraction. As shown in Figure 4-1, ZAJ values at all study sites from the two field trips are
exponentially related to the forest fractional cover. In sparse forests, there is more open
space at larger zenith angle (60°-75°) and, therefore, the values of LAI-Sring are lower
than LAI-4ring. Contrarily, in dense forests, the fractional cover is higher than gap
fraction and therefore, the values of LAI-Sring are higher than LAI-4ring.
Similar to the Z/1/-vegetation index relationships, the ZA/-forest fractional cover curve
begins to saturate at LAI around 3.0. However, the LAI values measured with fisheye
photos are unreasonably low in Figure 4-1. Even for the dense moist evergreen forests
with fractional cover higher than 95%, the ZA7 values are less than 4.0. The radiation
model in GLA software assumes that the atmosphere conditions above the forest canopies
are constant at the time of measurements at all of the study sites, which in fact varies
greatly depending on different seasons, amount of cloud cover or cloud properties. It
could be the possible reasons for the low ZA7 values in Figure 4-1.
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4.2.2
LAI ground measurements
From the radiation model in GLA software, the LAI and fractional cover are
logarithmically correlated (Figure 4-1). However, the Z,^/values in GLA software with
fisheye photos are underestimated. While the forest fractional cover can be measured
with fisheye photos, LAI-2000 provides a good measurement of LAI. Since it is
impossible to use LAI-2000 in Mae Chaem Watershed, I made additional measurements
during the third field trip in the hardwood forests in northern Michigan in October and
November 2002.
Between October 1 and November 12, 2002, four forested areas were visited and totally
42 study sites were measured (Table 4-1). These sites were close to river, lakes, or large
open areas so that the LAI-2000 requirements were met. At each study site, two open
readings and five under-canopy readings were recorded with LAI-2000 to measure LAI,
following the 1 s e q u e n c e . Three digital photos were taken with the fisheye lens
and were processed using GLA software to calculate fractional cover and LAI. As shown
in Figure 4-1, for forests with fractional cover lower than 95%, the LAI-4ring and LAI5ring values do not change much when calculated in GLA software. Therefore, only LAI4ring readings are compared in this analysis.
The study sites visited in early October simulates the canopy conditions of tropical
forests with higher cover, such as mixed deciduous and tropical evergreen in Mae Chaem
Watershed (in the dry season). The forests in northern Michigan are typical deciduous
broadleaf forests. In late October and early November, the leaves become yellowish and
84
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begin to drop off which is analogous for canopy conditions of dry dipterocarps and sparse
mixed deciduous forests (in the dry season) in Mae Chaem Watershed. Therefore, all
these study sites at different dates could approximately represent the ground
measurements in our study area in Mae Chaem Watershed and may provide ground data
for the LAI estimation. It should he noted that, the structure of tropical forests and
hardwood temperate forests are different. Some errors may he introduced with this
approximation.
The LAI vdihxes from LAI-2000 and fisheye photos over study sites in northern Michigan
were compared in Figure 4-2. The I ; 1 line was also drawn in the figure to demonstrate
the variation of the two LAI measurements. Although LAI values from fisheye photos are
a little higher than the ones from LAI-2000 for forests at low fractional cover, they are
much lower for forests with higher fractional cover. The LAI values from fisheye photos
increase very slowly when forest cover is higher. Among all the study sites with different
dates (leaf-on and leaf-off), the LAI-2000 measurements range from 0.88 to 5.04, while
the Z.T/values from fisheye photos {LAI 4-ring) only change from 1.39 to 2.66. Figure 42 confirms that fisheye photos are not a good source of Z,^/measurements.
4.2.3
LAI ~ forest fractional cover relationship
As discussed in Chapter 2 and 3, fisheye photos provide reasonable forest fractional
cover measurements. Replacing
values in Figure 4-1 with the LAI-2000
measurements, the relationship between LAI and forest fractional cover was shown in
Figure 4-3. Most forests in Michigan are old secondary or natural forests and there was
85
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no sparse forests found at the study sites. Therefore, the forest fractional cover values at
all study sites in Figure 4-3 are higher than 0.6. The L A Im A forest fractional cover
values fit an exponential relationship.
4.3
Model Development and Results
4.3.1 A modined Gaussian regression model
A modified Gaussian curve fitting model was developed with the ground measurements
in northern Michigan. In Figure 4-4a, both the LAI and forest fractional cover values are
extended to the origin to represent the sparse forests with fractional cover from 0 to 0.6, a
simulation o f dry dipterocarp forests in the dry season in Mae Chaem Watershed in
Thailand. The residuals of the regression are plotted in Figure 4-4b. Among the 42 study
sites, there are only four sites with absolute residual values larger than 1.0, and all these 4
sites have fractional cover around 0.85 or higher. Most of the residuals are distributed
between ±0.5. Figure 4-4 also indicates that the higher the forest fractional cover, the
higher the residuals. It is reasonable because the LAI-2000 measured LA I is a measure of
plant projected area instead of green foliage area. As the fractional cover becomes higher,
there introduces more error in the LAI-2000 readings.
The regression equations are expressed as:
Zyl/ = 0.217 +0.058
0 .6 < /c < 1 .0
LAI = 0.85 * fc
0.0 < fc < 0.6
(4-1)
Here fc is the forest fractional cover at the scale of [0,1]. There was no groimd data when
fc is lower than 0.6. However, from Figure 4-2, the relationship between LAI and
86
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fractional cover is almost linear when the fractional cover is low. Therefore, a linear
relationship in Eq.4-1 was used for 0.0 < fc < 0.6.
A
goodness-of-fit test was examined to the modified Gaussian regression. The
was
calculated from:
l a lexp
Here N is the total number of ground measurements, N=42 in the study sites in the
northern Miehigan.
is LAI-2000 measurements, LAI^^^ is the modeled
values
from Eq.4-1. At the degree of freedom of 38 (N-4 coefficients in the model) and
confidence level of 95%, the critical value, xl.os,3 i »is 55.76. As shown in Figure 4-4a,
the x^ value in this test is 9.112, far less than xlos 38 • Therefore, the null hypothesis
cannot be rejected and the modified Gaussian regression model is valid. Moreover, the
correlation coefficient (R) between modeled and measured
values is 0.895, also
indicating that the model could characterize the LAI ~ forest fractional cover relationship
very well.
4.3.2
LAI estimation
The forest fractional cover distribution in Mae Chaem Watershed in Thailand has been
mapped in chapter 3. With the modified Gaussian regression model, the LAI distribution
could be mapped. Figure 4-5 is the Z-^7 distribution in the study area in dry and the wet
seasons, resulting from the forest fractional cover distribution in both seasons. The open
areas that are non-forests are masked out.
87
R eproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Similar to that of forest fractional cover in Chapter 3, the seasonal change o i LAI
distribution is significant. In the dry season, the leaves on deciduous trees become
senescent and were partially off and, therefore, most of the dry dipterocarps and mixed
deciduous forests have very low Z.^7 values (less than 1.0). The tropical evergreen forests
have Z.T/values 4.0 or higher. In moist evergreen forests, the Z.^7 values saturate at
around 10.9. In Figure 4-5a, there is a very narrow zone between mixed deciduous and
tropical evergreen forests where the 7.T7 values are around 1.0 (corresponding to the
fractional cover 70-80%). It could be the pine zone transition where the seasonal change
is less significant than deciduous or mixed deciduous forests, but more significant than
tropical evergreen forests.
In the wet season, the tree leaves are green and healthy, and most of the dry dipterocarps
and mixed deciduous forests have Z.T7higher than 1.0 (Figure 4-5b). The narrow zone in
Figure 4-5a merges to the tropical evergreen forests with Z.T7 greater than 4.0. The LAI
increment in tropical evergreen forests is not as significant as other forest types, but there
are more saturated areas than those in the dry season. The open areas in Figure 4-5b are
human settlements (town, villages) and agricultural fields. Surrounding these open areas,
there are some forests that have Z^7 values far less than I.O in the wet season. Most of
these forests are sparse young regrowth of dry dipterocarps and are highly disturbed by
human activities. Therefore, the Z^47 values in these forests are very low even in the wet
season.
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4.4
Validation
As discussed earlier, because of the physical difficulty to access the study sites and the
lack of open areas in deep forests, it is difficult to measure the leaf area index over the
study sites in tropical forests with LAI-2000. As a result, it is impossible to directly
validate the modeled LAI distribution mapped in this study.
As an alternative, an indirect validation was made in this section. The leaf area index
(LAI-4ring) can be calculated from the fisheye photos over the study sites in Mae Chaem
Watershed in northern Thailand. As shown in Figure 4-2, although the LAI from fisheye
photos were not as sensitive as that of LAI-2000, there was a linear relationship between
these two LAIvdXuQS in the LA/range between 0 and 6. With this linear relationship, the
LAI-4ring values at the study sites in the watershed in Thailand could be adjusted to the
equivalent values of LAI-2000 measurements. These adjusted LA/measurements were
assumed as ground “truth” to validate the modeled LA/distribution in this study. To be
consistent with the modeled LAI distribution in the dry season, only the fisheye LAI-4ring
values at the 32 study sites during the second field trip were adjusted.
The comparison of the modeled and adjusted measured LA/values at the study sites in
the watershed was shown in Figure 4-6. In the LA/range between 0 and 4, the modeled
LAI fitted with them well and the data were scattered along the 1:1 line. The adjusted LAI
was underestimated when the ground LAI
higher. The adjusted LA I was still less than
4 when the modeled LA/reached 6. In moist evergreen forests, the modeled LA/values
89
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were saturated at 10.9 as shown in the Z^/m ap, while the adjusted measured Z^/values
were only around 5.
The adjusted measured LAI from fisheye photos was not the real ground truth. Since the
fisheye measured Z ^/w as adjusted from that of LAI-2000 measurement when LAI was
less than 6.0, it did not provide good validation when the Zt4/was higher than 6.0 in the
watershed. When Z.T/was less than 6.0, the adjusted measured ZA7 could be applied for
the purpose o f validation. The linear regression line between modeled and adjusted
measured Z.T/was very close to the 1:1 line and the square correlation coefficient
=
0.67 (Figure 4-7).
4.5
Conclusion and Discussion
In this chapter, a modified Gaussian regression model was built to simulate the
relationship between LAI and forest fractional cover in the study area. The LAI values
were measured from the LAI-2000, and forest fractional cover values were from fisheye
photos as discussed in Chapter 3. In the
goodness-of-fit test, the actual
from the
model was only 9.71, far less than the critical ^'o.os.ss (55.76) at the 95% confidence level.
The correlation coefficient between the modeled and ground-measured LAI values was as
high as 0.90. Most of the residuals distributed between +0.5 and only 4 out of the 42
ground m easurem en ts had residuals higher
than 1.0. All th ese statistieal an alyses
supported that the model was valid and could be applied to estimate LAI distribution with
forest fractional cover product in Chapter 3.
90
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The LAI distributions in both dry and wet seasons were mapped in this study. The
seasonal change of the LAI distributions was significant. Most of the dry dipterocarps and
mixed deciduous forests have 1/1/values less than 1.0 in the dry season while higher than
1.0 in the wet season. The Z.T/values of the tropical evergreen forests were always
around 4.0 and higher in both seasons, but there were more areas that were saturated
because o f the forest fractional cover saturation. The Z^/values in saturated areas were
higher than 10.0. A narrow zone with Z/1/around 1.0 was shown in the Z ^/m ap in the
dry season, possibly the pine transition zone whose seasonal variation was lower than
deciduous forests but higher than tropical evergreen. On the other hand, some forests
around the open areas (villages, agricultural areas) have very low LAI values even in the
wet season, indicating intense human disturbances in these forests.
The method in this study provided a new approach to map LAI with forest fractional
cover which could be estimated from remotely sensed data. It should be noted, however,
the ground data were not measured in the study area in Thailand. Because of the
topographical difficulty, as well as the limited open area in deep tropical forests, it is
impossible to make LAI-2000 measurements in mountainous tropical forests in Mae
Chaem Watershed. Instead, some deciduous forests in northern Michigan in different
seasons (leaf-on and leaf-off) were selected to approximately represent the tropical
forests in the study area. Since the gap fraction is the major key to calculate LAI and
forest fractional cover values, this alternative is acceptable if the fractional cover has
large range of variation, e.g. from sparse to dense forests at the study sites in northern
91
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Michigan. The ground measurements in this study lacked of forest samples with sparse
forests that should be considered in the future research.
In this study, it was found that although the GLA software also gave the readings of LAI
(4-ring or 5-ring), these ZA7 values are significantly underestimated. LAI-2000
measurements are more realistic than LA/values from GLA with fisheye photos.
Theoretically, the GLA/fisheye photo and LAI-2000 apply the same radiation model. The
primary difference is that fisheye photo assumes that the atmospheric conditions are
constant at all seasons and under all cloud cover properties, whereas the LAI-2000 makes
one or two open readings simultaneously with the canopy readings. The gap fraction of a
fisheye photo is calculated with binary image clustering techniques. The gap fraction of
LAI-2000, however, is the ratio of the canopy readings to the open readings. If this
difference can be compensated for the fisheye photo and GLA software, it is possible to
make reasonable LA/as well as fractional cover measurements with fisheye photos. Then
digital cameras with fisheye lens will become a more economic and efficient tool for
ground measurements in tall canopies, especially the dense mountainous forests where
LAI-2000 is in limited usage.
92
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4.6
Reference
Baret, F. (1995). Use of spectral reflectance variation to retrieve canopy biophysical
characteristics. In Advances in Environmental Remote Sensing (M. Darson and S.
Plummer, E d s.), John Wiley and Sons, Inc., 34-51.
Baret, F. and Guyot, G. (1991). Potentials and limits of vegetation indices for Lai and
APAR assessment. Remote Sensing of Environment: 35,161-173.
Best, R. G. and Harlan, J. C. (1985). Spectral estimation of green leaf area index of oats.
Remote Sensing of Environment: 17,27-36.
Frazer, G. W., Canham, C. D. and Lertzman, K. P. (1999). Gap Light Analyzer (GLA),
Version 2.0; Imaging software to extract canopy structure and gap light
transmission indices from true-color fisheye photographs, users manual and
program documentation. Copyright © 1999: Simon Fraser University, Burnaby,
British Columbia, Canada, and the Institute of Ecosystem Studies, Millbrook,
New York, USA.
Goel, N. S. and Thompson, R. L. (1984a). Inversion of vegetation canopy reflectance
models for estimating agronomic variables. IV: Total inversion o f the SAIL
model. Remote Sensing of Environment: 15, 237-253.
Goel, N. S. and Thompson, R. L. (1984b). Inversion of vegetation canopy reflectance
models for estimating agronomic variables. V: Estimation of LAI and average leaf
angle using measured canopy reflectances. Remote Sensing of Environment: 16,
69-85.
Peterson, D. L., Spanner, M. A., Running, S. W. and Teuber, K. B. (1987). Relationship
of thematic mapper simulator data to leaf area index of temperate coniferous
forest. Remote Sensing of Environment: 22, 324-341.
Pinty, B., Verstraete, M. M. and Dickinson, R. E. (1990). A physical model of the
bidirectional reflectance of vegetation canopies, 2: Inversion and validation.
Joumalof Geophvsical Research: 95(D8), 11,767-11,775.
Qi, J., Cabot, F., Moran, M. S. and Dedieu, G. (1995). Biophysical parameter estimations
using multidirectional spectral measurements. Remote Sensing of Environment:
54, 71-83.
Qi, J., Kerr, Y. H., Moran, M. S., Weltz, M., Huete, A. R., Sorooshian, S. and Bryant, R.
(2000). Leaf area index estimates using remotely sensed data and BRDF models
in a semiarid region. Remote Sensing of Environment: 73: 18-30.
Sellers, P.J. and Schimel, D. (1993). Remote sensing of the land biosphere and
biochemistry in the EOS era: science priorities, methods and implementation -
93
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EOS land biosphere and biogeochemical panels. Global and Planetary Change: 7,
279-297.
Spanner, M., Johnson, L. and Miller, J. (1994). Remote sensing of seasonal leaf area
index across the Oregon transect. Ecological Applications: 4(2), 258-281.
Strahler, A. H. (1994). Vegetation canopy reflectance modeling - recent developments
and remote sensing perspectives. Remote Sensing Reviews: 15, 179-194.
Tucker, C.J., Holben, B. N., Elgin, J. H. and McMurtrey, E. (1981). Remote sensing of
total dry matter accumulation in winter wheat. Remote Sensing of Environment:
11, 171-190.
Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht, K. S. and Briggs, J. M. (1999).
Relationships between leaf area index and Landsat TM spectral vegetation indices
across three temperate zone sites. Remote Sensing of Environment: 70, 52-68.
Welles, J. M. (1990). Some indirect methods of estimating canopy structure. Remote
Sensing Reviews: 5(1), 31-43.
Welles, J. M. and Norman, J. M. (1991). Instrument for indirect measurement of canopy
architecture. Agronomv Journal: 83, 818-825.
Welles, J. M. and Cohen, S. (1996). Canopy structure measurement by gap fraction
analysis using commercial instrumentation. Journal o f Experimental Botanv:
47(302), 1335-1342.
94
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Table 4-1
Study sites in northem Michigan.
Study area
GPS Location
(UTM, WGS84)
Date
Study sites
Muskegon River
Watershed
(552741, 4789298)
10/1/2002
5
Burchfield Park
(697576,4720093)
10/2/2002
5
Rose Lake Wildlife
Conservation
(716891,4742766)
10/5/2002
16
11/12/2002
10
MSU baker
woodlot
(706798,4732483)
11/12/2002
6
95
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4
o LAI-5ring
X LAI-4ring
3
2
1
0
0.2
Figure 4-1
0.4
0.6
0.8
forest canopy cover
1.0
Relationships between LAI (5-ring and 4-ring) with forest fractional
cover. The fractional cover is in the range of [0,1].
6
5
4
u.
3
oo
2
1
0
0
1
2
3
4
5
6
LAI (LAI-2000)
Figure 4-2
Comparison o f LAI measurements from LAI-2000 and fisheye photos.
The 1:1 line is also drawn in the plot.
96
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6
5
- c
o
4
o
o
o
-O............
O
CN
<
cP^o
%)
o
3
o
O
<
O o
2
1
o
Cc
......... „ . . . Q
0
0.65
^
0
o
0.7
0.75
0
0.8
0.85
0.9
0.95
forest canopy cover (Fisheye)
LAI ~ forest fractional cover relationships measured with LAI-
Figure 4-3
2000 and fisheye photos in northem Michigan.
6
5
o£4
<
4
1.5
y = m4+mrexp(m2*(m0-m3)''2)
Value
Error
0.058
6.164
5.578
m2
m3
0.033
20.171
m4
0.217
6.885
Chlsq
9.712
NA
0.895
NA
o
c
1
o
°
0.5
©
0
3
0
;
I
2
-0.5
1
-1
3
g
oO
o
o
r
.......
^
C ® CS) 0
o
o
o o
o
o
^
O 0
-1.5
0.65
0
0.2
0.4
0.6
0.8
0.75
0.8
0.85
0.9
forest canopy cover
forest canopy cover
(a)
Figure 4-4
L ...................
0.7
(b)
A modified Gaussian curve fitting (a) and its residual plot (b).
97
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0.95
%
LAI
I
■>4
(b)
Figure 4-5
LAI distribution in the study area in dry (a) and wet (b) seasons.
98
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12
10
8
■D
0> 6
(1)
T3
O
4
2
0
2
4
6
8
10
12
adjusted fisheye LAI
Figure 4-6
Comparison of modeled and adjusted measured LAI over the study sites in
the watershed (second trip).
6
5
4
T3
« 3
0)
■a
2
1
R2=0.67
0
0
1
2
3
4
5
6
adjusted fisheye LAI
Figure 4-7
Correlation between modeled and adjusted measured LAI over the study
sites with LAI <6.
99
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Chapter 5
A Microwave/Optical Synergistic Canopy Scattering Model
and its Inversion to Estimate Forest Structure
5.1
Introduction
Forest canopy structure is the combination of forest texture (the qualitative and
quantitative composition of the vegetation as to different morphological elements) and
forest structure (the spatial arrangement of these elements) (Barkman 1979). Forest
structural parameters are important input variables in ecosystem functioning and forest
harvest models. The understanding of forest structure is also necessary in accurate
estimation of forest biomass. Depending on the study interest, the forest canopy structure
can be described at several levels of integration: forest/non-forest at large scales, forest
types in different communities, within-forest patches (e.g., successional developments
phases, leaf area index), individuals (e.g., species and tree size, height), plant parts (e.g.,
crown and stem), and plant organs aboveground (leaves, branches, flowers, etc)
(Bongers, 2001). From this definition, both forest fractional cover and leaf area index are
structural parameters. They are described in Chapter 3 and Chapter 4 because they are all
green leaf properties and have been intensively studied using optical remote sensing. The
forest structural parameters described in this chapter are mainly woody structural
properties such as tree height, stem height, diameter at breast height (DBH), and forest
stand density.
100
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optical remote sensing techniques have been applied in the retrieval of forest structural
parameters. Tall vegetation species create larger shadows, but they also expose a larger
portion of their vertical structure to the sensing systems when viewed from off-nadir
directions. Consequently, optical sensors with off-nadir view angles can be used to study
the vegetation biophysical structures (Qi et al. 1995). Li and Strahler (1992) built a
geometric-optical model in which a tree canopy was a collection o f individual geometric
objects that cast shadows on a contrasting background: sunlit crown, shaded erown, sunlit
background and shaded background. In this model, the three-dimensional structure of the
canopy is the primary factor influencing the reflectance from the canopy (Strahler and
Jupp 1990).
A good way of forest structure retrieval with optical remote sensing data is the inversion
of the geometric-optical model (Woodcock et al. 1994; Shimabukuro and Huemmrich
1995). Scarth and Phinn (2000) defined forest structure as the horizontal and vertical
distribution of components within a plant community. They applied TM images, DEM,
and field measurements in an inverted Geometric-Optical model (Li and Strahler 1992) to
estimate crown diameter, tree density, crown cover projection, and the “treeness”
parameter. Recent developments in high spatial resolution remote sensing techniques
leads to small scale characterization of canopy structure such as small forest gaps and
individual crown characteristics. These structural properties are important in monitoring
forest changes resulting from selective logging activities (Bongers 2001).
101
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Microwave remote sensing techniques also have been applied to retrieve structural
information in temperate forests. Previous studies have demonstrated that forest structural
variation may have a substantial effect on L-band backscatter of forest stands with same
biomass (Imhoff 1995). The spatial pattern such as inter-tree gaps and tree clumping in
uneven forests may create significant differences in radar backscattering at finer
resolution (Sun and Ranson 1998).
The forest structural information retrieved from SAR backscattering is wavelengthdependent. Signals with shorter wavelength (e.g. X- and C-bands) mainly contain
information in crown layers. More information about woody structures and biomass
could be retrieved from longer wavelengths (e.g. L- and P-hand) because the signals can
penetrate the crown layer (Le Toan et al. 1992). The backscattering is also polarizationdependent. The backscattering in each polarization contains different structure
information. HH-polarized backscattering is dominated by the ground-canopy interaction
mechanism while direct crown backscattering contributes more significantly to the VVand HV-polarized backscattering (McDonald and Ulaby 1993). Karam et al. (1995) also
showed that the HV backscattering coefficient was due almost solely to the long branches
at all ages. Therefore, the dynamic range observed in HV polarization reflected the
physiological changes of long branches as the canopy age increased.
The phase difference (the phase of the complex polarimetric coherence) in polarimetric
SAR data, resulting from the crown attenuation and stalk-ground double bounce, contains
much forest structural information (Ulaby et al. 1987). Phase difference could be
102
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calculated in a polarimetric backscattering model (Ulaby et al. 1990). Hoekman and
Quinones (2002) proposed a new method of polarimetric coherence decomposition
instead o f power to relate polarimetric signal and forest structures.
Structural information can also be described by image texture, the spatial variability of
image tones that describes the relationship between elements of surface cover (Wulder et
al. 1998). For images over forests, the variation in texture is related to changes in the
spatial distribution of forest canopies. The medium to coarse resolution optical remote
sensing imagery often has a smooth texture so that the information in texture variation is
limited. However, the high resolution imagery such as IKONOS is able to reveal detailed
variation o f image texture. These data can be used to detect internal stand shade
conditions as mutual shadowing that is more dominant in mature forest than secondary
regrowth. SAR signal penetrates forest canopies much deeper than sunlight and therefore,
contains more structural information than optical imagery. Luckman et al. (1997) showed
that coefficient of variation (CV) of SAR backscattering intensity exhibited the least
variation within each regrowth age, and was the best method to measure texture. Aside
from the deficiency of speckle noise, the standard deviation of SAR intensity and SAR
image texture is useful in forest structure retrieval (Manninen and Ulandder 2001).
In this chapter, a micro wave/optical synergistic radiative transfer model was developed
and the backscattering from forest components was simulated while the leaf properties
were measured from optical remotely sensed data. The forest structures were the input
parameters of the model. The model was validated with JERS-1 SAR and VNIR data and
103
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ground measurements. Then, with a nonlinear least square (LS) optimization method, the
model was inverted and the forest structures estimated. Finally, an error analysis was
performed to examine the accuracy of the model inversion.
5.2
Model Development
The forest can be simplified as a three-layer vegetation composition: leaf canopy,
branch+leaf canopy, and trunk atop of a rough soil surface (Figure 5-1). For different
forest types, the components in each layer and the values of their biophysical parameters
may vary. The saplings and seedlings and the grass/shrub underneath the forest are not
considered in the simulation. The backscatter intensity is the result of additive
contribution from the components in all layers:
^ to ta l ~ ^ la y e r l
^ la y e r ! ^ ^ la y e r !
^ so il
(5 -1 )
In a 2"‘’-order solution of radiative transfer equations, the total backscatter consists of
surface scattering from soil underneath, volume scattering from leaves, branches, trunks,
interaction between forest components and soil surface, and 2"‘'-order non-coherent
scattering:
^ to ta l
^ so il
^ le a f
^ lea f -s o il
^b ra n ch
^ branch-soil
^ trunk
^ tru n k-so il
^n o n c o h e re n t
The microwave canopy scattering model developed by Karam et al. (1995) had a
detailed explanation of the scattering properties of the canopy-related components in
forests. However, in Karam’s model, the
in Eq.5-2 is a very simple H*-order
solution of radiative transfer equations, assuming the soil as a continuous and slightly
rough dielectric surface. It is thus insufficient to simulate radar backscatter under various
104
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soil roughness and moisture conditions in different forest types, particularly in sparse,
young, secondary forests in which the soil surface plays an important role in determining
the total backscatter.
Three modifications to Karam’s model were made in this study. The first was to
introduce an integral equations model (lEM) bare surface scattering model (Fung et al.
1992) into the canopy scattering model. The second was to link the model to optical
remote sensing variables. Since Karam’s model was originally developed for conifer
forests, the probability distribution function (PDF) of each of the forest components was
also modified to fit the characteristics of tropical forests in the study area.
5.2.1
Modified Karam-IEM model
The first-order solutions of radiative transfer equations for backscattering coefficients (in
power units) o f the components in Fq.5-2 are described in Fq.5-3 to Fq.5-12. The
second-order solutions of the non-coherent backscattering are much smaller than the
first-order ones and, therefore, the equations are not listed in this study.
5.2.1.1
Soil surface scattering
Soil surface backscattering in forests depends on its dielectric constant, surface
roughness, and attenuation from the forest canopies above the soil surface. Surface
roughness can be described with a root-mean-square (rms) of the surface roughness
height and an auto-correlation function. The scattering coefficient of soil ground
scattering (in power units) can be expressed as:
105
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^ s o il ~
( 5"3)
pq'^^q'^lq'^Xq
where p and q are polarizations (H or V), r- is the polarized attenuation factor from the
ith layer (leaf canopy, branch+leaf, and trunk), and cr^^ is the pq element of the surface
scattering matrix given in the IBM model (Fung et al. 1992).
The IBM model extends its application to a wide range of soil surface conditions. It
reduces to the small perturbation model when the surface is slightly rough and to the
Kirchhoff model when surface roughness is large. Based on an approximate 2"‘^-order
solution of a pair of integral equations for the tangential surface fields, the soil surface
scattering is composed of single and multiple scattering when the surface rms slope is
large:
_
PI
sm g le
multiple
pq
pq
V
'
Since most natural terrain has a small rms slope, single scattering dominates over the
multiple scattering in most situations (Shi et al. 1997). The single scattering coefficient
(in power units) can be described as (Fung et al. 1992):
2 W \- 2 k ,,Q )
pq
9
n=\
where k is the wave number, k ^ - k cos 77, k^ = A:sin 7 , 7 is incident angle, s is the rms
height of the soil surface, / is the scattering intensity, and W" is the Fourier transform of
the nth power o f the surface auto-correlation function.
106
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Multiple scattering is significant only when the surface rms slope is large. It dominates
the cross-polarization backscattering. The multiple scattering coefficient (in power unit)
can be described as (Fung et al. 1992):
T2
, -
^ m u ltip le _
2 \n + m
)
00
CO
'SP
n=[ m=i
|R e [/^*^F p ^ ( u
I 2
-
1 Fpa ( u , v )
-
,v ) ]w ^ ( u - k ^ ,v ) - W "'(k ^
00
2
+
CO
+ u,v)d u d v
f j 2 2\n+m
*
♦
(
m
,
v
) F „ „
i - u - v )
■ W ^ ( u - k ^ ,
v )r"
{u + k ^ , v )d u d v
(5-6)
where
and
are the coefficients of the Kirchhoff component and its complementary
component in the electromagnetic field on soil surface. They are described in detail in
Fung et al. (1992).
5.2.1.2
Leaf scattering
In Eq.5-2, the leaf volume scattering coefficient (in power unit),
contribution from all of the leaves in the first {<y]g„f) and second
It is a
, is an additive
layers in forests.
order solution of the radiative transfer equation (Karam et al. 1995):
1
^ le a f
^ le a f
2
^ leaf
(^ip+^i?)sec7
(^2p+^2„)sec7
where k^ (i-\,2,j= p, q) is the extinction coefficient in the rth layer andyth polarization.
Q‘p f^ and Q‘p f^ represent scattering from one leaf within layer 1 and layer2.
107
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QTI =
•y ^pq
I ’ where
is the element of the scattering amplitude matrix for a
leaf, and nj is the number of leaves in this layer. The ensemble average <> is used in the
equation to represent the statistical average over all leaves in this layer.
5.2.1.3
Branch scattering
Similarly, the branch volume scattering in Eq.5-2,
, is the
order solution of
radiative transfer equation describing the scattering from branches in the second layer:
» „ = 4 ; = G " ' 7 r ^ 7 T 4 ^ ------ V . .
Here
= «2 ’ (
PQ
PQ
(5-8)
is the element of the scattering amplitude matrix for
a branch in layer2, and ri2 is the number of branches in this layer.
5.2.1.4
Trunk scattering
The description of trunk volume scattering in Eq.5-2,
, is similar with Eq.5-7 and
Eq.5-8 except the attenuation factors:
Here
trunk
7 trunk
=n^- i F^™" ), F'™"*
is the element of the scattering amplitude matrix for a
PQ
branch in layer3, and ri3 is the number of trunks in this layer. The volume backscattering
of trunks suffer from the attenuation from both layer 1 ( Tj ) and layer2 ( Tj ).
108
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5.2.1.5
Leaf-soil interaction
When calculating the interactions between scatterors in different layers, the soil surface is
assumed to be a specular or a slightly rough surface to simplify the radiative transfer
equations. In Eq.5-2, the backscattering coefficient caused by the interaction between
leaves and soil surface,
, is an additive contribution from the leaves in first (leaf
canopy) and second (leaf+branch) layers. It can be described as:
^leaf-soil
^leafl-soil
leaf2-soil
where
<^ieafx-soii =4;rcos;7-r,^r2^r3^
^\q
^leafi-soii = 4^ COST] ■
^\p
+RJ^2q
^2p
Here R^^ and R^^ are the Fresnel reflectivity of the soil surface at /? or ^ polarization.
5.2.1.6
Branch-soil interaction
Similarly, o'branch-soil
Efl-5-2 can be described as:
( ^ b r a n c h - s o l l = ^ ^ C O S 1 ] - T , ^ T , ^ r , ^
. ^ - 4 ^ V cos^, .
■ R ^ ^
^2q
52.1.1
^2p
Trunk-soil interaction
Similarly,
in Eq.5-2 can be described as:
(^irunk-soU = 4;T COS7/ •
■R^p •
(5-12)
^3q
^2p
109
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5.2.2
Linkage to optical remotely sensed variables
Optical remotely sensed data have been successfully applied to extract green forest
biophysical attributes such as leaf area index {LAI), an indicator of green leaf biomass.
The amount of green leaves controls the magnitude of attenuation from the leaf canopy
in the canopy scattering model. Therefore, linking optical remote sensing variables to the
model could provide quantitative information for the estimation of forest woody
biomass.
As described in Eq.5-3 to Eq.5-12, there is an attenuation factor in each layer of the
forest (two factors in the second layer coming from the branches and leaves,
respectively). Assuming the layer height is H, the attenuation factor in either forward or
backward direction could be described as:
I cos,
where t= p or q, the polarization status of the signal. The extinction coefficient (A:,) is
the total extinction cross section of all leaves statistically averaged over the orientation
probability distribution:
A ttN
(5-14)
K
where
is the co-polarization component of the scattering matrix, or scattering
amplitude tensor, of a single scatteror. N is the density
of the scatterors in this
layer.
110
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In the canopy scattering model, the leaf is modeled as an ellipse. Let a and b be the halflength and half-width of an elliptic leaf respectively, the leaf density number N in Eq.514 can be related to the leaf area index {LAI) by:
= ------ — -----mh{H, +H^)
(5-15)
It was assumed the same leaf density in first and second layers. In tropical forests, LAI
can be estimated from green fractional cover (fc) that is derived from optical remotely
sensed data as described in chapter 4;
= 0.217 +
0
.
0
L ^ / = 0.85-/c
5
8
0. 4 </ c <1 . 0
(5-16)
0</c<0.4
MSL4L/-MSL4F7
where fc = ------------------------- ------in a linear unmixing model as described in
Chapter 3. The value offc is in the range of [0,1]. M SAV Iis the Modified Soil Adjusted
Vegetation Index and the detailed description could be found in Qi et al. (1994).
5.2.3
PDF functions of forest components
In the canopy scattering model, the total backscattering in one layer is the statistical
addition o f the backscattering from all scatterors with a probability distribution function
(PDF). As shown in Figure 5-2, the distribution o f leaves in tropical forests is assumed to
be approximately spherical, the branches are more plagiophile, and the trunks are almost
vertical. T he
PDF equations o f each forest com p on en t are listed b elow :
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[O,^]
= |s in 2a|/2
™ ^ ; „ „ . = | cos3 « |/ A
(5 -1 7 )
4
Lo
P D F „ „ = co s‘ a A
lo
o
[0.;r]
where a is the inclination angle of scatterors. For elliptic leaves, it is the angle between
vertical and the normal vector of the disc in clock-wise direction. Branches and trunks are
simulated as cylinders and a is the angle between vertical and the axis of the cylinder in
clock-wise direction. The denominators in Eq.5-17 are the normalization factors that are
the integration of each function in the limited range of a values.
5.3
Model Simulation and Validation
By replacing the simple soil scattering model with the IBM model and linking some
biophysical parameters to optical data, a microwave/optical sjmergistic canopy scattering
model was developed to simulate scattering from tropical forests. The study area is Mae
Chaem Watershed as described in Chapter 2. The remotely sensed data in microwave
spectrum were JERS-1 Synthetic Aperture Radar (SAR) data, and optical spectrum were
JERS-1 VNIR (visible + near infrared) data. Only backscatter in L-band and HH
polarization with incidence angle of 35° was modeled in order to match the JERS-1 SAR
configuration.
5.3.1 Remotely sensed data
The study area covers two scenes of JERS-1 SAR data (path/row): 132/269 and 132/270.
Two scenes of JERS-1 VNIR data in the same orbit were also acquired. The JERS-1 SAR
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is an active sensor that transmits L-band microwave signals and detects the characteristics
on the Earth surfaee with the backscattered signals. JERS-1 VNIR is a high-resolution
radiometer that observes targets by receiving solar radiation reflected from the earth
surface in three visible and near-infrared (NIR) bands. The system parameters of these
sensors are listed in Table 5-1. The SAR data was acquired 8 days earlier than the VNIR
data. The surfaee change during this period was thus neglected.
The two scenes o f SAR or VNIR data are in the same orbit and therefore, it is easy to
mosaic them into a larger scene (Figure 5-3). Both SAR and VflSlR mosaic image covered
most of the study area with gaps on the east and west edges. With the information in the
metadata, both SAR and VNIR images were georefereced using UTM projection (zone
47) with spheroid and datum WGS84. There was no cloud cover in the VNIR images.
The atmospheric effect in VNIR images was corrected with a simple dark-objectsubtraction (DOS) technique (Chavez 1988).
SAR and VNIR images were geometrically corrected by setting the reference image as
the ETM-i- image acquired on February 2, 2000, which has been geometrically corrected
with ground control points (GCP) collected during field trips. From a nadir view, the
VNIR images did not have much geometrical distortion. The total error was 6.14m in a
second-order polynomial geometric model. The JERS-1 SAR is a side-looking sensor
with a shallow incident angle. As a result, its imagery is severely distorted in the
mountainous area.
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SAR data was processed with a 3x3 Lee filter to reduce the speekle noise and increase the
signal/noise ratio with inereased number of looks. During the geometric correction,
considering the large area of the coverage and the severe topographic variation, the study
area was divided into four parts with a narrow overlay zone. Each part of the data was
geometrically corrected by setting the ETM+ image as the reference. In a 3'^‘’-order
polynomial geometric model, the total errors are 51.68, 25.23, 42.13, and 48.78 meters
for the lower right, upper left, upper right, and lower left parts, respectively. Sinee the
look direction of JERS-1 SAR was from west to east, the errors in west-east direction
were much higher than north-south direction. The four parts were then mosaiced again
(Fig.5-4b). Even splitting the total area into 4 sub-areas, it is still difficult to correct the
geometric distortion in the watershed. In the areas with high elevation and slopes (Fig.54a), the geometrie correction has very high error. To constrain the total error in one pixel,
the pixel size o f geometrieally corrected SAR data was resampled to 60 by 60m with a
nearest neighbor method.
SAR backscattering was also affected by the topographic variation in mountainous areas.
There are several types of topographic distortions in the SAR image: foreshortening,
layover, and radar shadow (Hendenson and Lewis, 1998). Foreshortening occurs when
the ground range difference between two ground positions is reduced to the slant range
differenee, which is shorter because of the side-looking characteristics of SAR and the
topography on the ground. When the shortening becomes greater, the position on the top
of the mountain will be closer to the antenna than the position on the bottom, then
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layover occurs. In this case, the radar signal cannot access the opposite slope, and radar
shadow occurs.
The foreshortening effect can be corrected with DEM data. During topographic
correction, the backscattering amplitude, DN, of the SAR image can be corrected to a
reference surface on which the local incidence angle of each pixel is 0 (Van Zyl et al.
1993):
(5-18)
sm/7o
where
is SAR incidence angle,
rj is local incidence angle, and6^is azimuth slope.
Setting 9 as local slope angle and A^y as the relative azimuth angle between local aspect
and SAR azimuth, local incidence angle rj is calculated:
cos rj = cos 0 cos //g + sin 6 sin ?7g cos is.(p
(5-19)
The azimuth slope 9^ can be solved from tri-angle relationships:
tg9^ = tg9 sin A(p
(5-20)
In the areas with layover and radar shadow, the SAR data were permanently lost and
cannot be corrected. The conditions when layover and radar shadow occur are:
Layover:
rj < 90° and 9 <?j
Radar shadow:
?j >90°
Figure 5-5 is the local incidence angle (a) and the topographically corrected image (b).
The areas with layovers and shadows were shown as white color in the local incidence
angle image. The backscattering amplitude in these areas cannot be used in the following
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analysis. In high mountainous areas with large local incidence angles, although the
layovers and shadows did not occur, the foreshortening effect was so severe that the
correction accuracy was very poor. For this reason, the application of JERS-1 SAR data
in mountainous area is limited.
5.3.2
Model simulation
The model was simulated with a set of parameters listed in Table 5-2. The range of each
parameter was determined with the experience gained during field trips. The mean of the
parameter was selected approximately based on the frequency of occurrences. To
examine the contribution of these biophysical parameters on the backscattering, only the
values of one parameter was changed during each simulation, while other parameters
were represented by their mean values. With the configuration in Table 5-2, in L-band
HH polarization, branch volume scattering, trunk-surface double bounce, and leaf volume
scattering are the primary components in total backscattering (Figure 5-6).
5.3.2.1
Contribution of leaves
In the microwave/optical synergistic forest scattering model, the leaves are simulated as
elliptic discs of which ai, aj are the radii of long and short axis, and r=al/a2 is the elliptic
ratio of the leaf. Figure 5-6a shows the variation of backscattering in different leaf size
and elliptic ratio. When both the leaf size and elliptic ratio are small (ai=0.02m and
r=l .2), the backscattering coefficient is very low and then increases rapidly with higher
elliptic ratio. When the leaf size is larger, the backscattering decreases slowly with
elliptic ratio. With much larger leaves (ai=0.07), there is almost no difference in the
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backscattering with the change of elliptic ratio. The backscattering difference in the range
of leaf sizes could be more than 2dB in small elliptic ratio (r=1.2) while only 0.3dB in
large ratio (r=1.5). Therefore, a larger leaf size (ai=0.05) and elliptic ratio (r=1.5) is used
in the following simulations.
The contribution o f different components in the forest model is shown in Figure 5-6b
with the change o f LAI, which is directly related to leaf size and canopy height (Eq.5-15).
When LAI values increase, both the leaf volume scattering and leaf-soil interactions
increase rapidly then slow down to reach the saturation point. The contributions of other
components decrease due to the attenuation from the leaves. As described in Eq.5-13 and
5-14, the attenuation loss in dB unit is more or less linearly related to the amount of
leaves. With the configuration listed in Table 5-2, the increased backscattering from
leaves are compensated with the backscattering loss from other components, and
therefore, the total backscattering increase is only IdB in Figure 5-6b.
5.3.2.2
Contribution of branches
The branch-related backscattering varies with both branch size and density. The branch
length is assumed to be same as the height of layer 2. As shown in Figure 5-6c, in L-band
HH polarization, the modeled branch volume scattering and branch-soil interaction are
very sensitive to branch radius. The branch volume scattering has the highest value at
branch radius = 0.018m and lowest value at radius = 0.028m, and the difference is as high
as 8dB. The branch-soil interaction has an obvious low value at radius = 0.033m, and the
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difference between highest and lowest scattering is around 3dB. The backscattering of
other components changes with a much smaller scale.
With the increase of branch density (# W ), both the branch volume scattering and
branch-soil interaction have higher values, and the increment is 5dB and 3dB,
respectively (Figure 5-6d). The branch-soil interaction is more quickly to reach
saturation. The scattering of other components decreases almost linearly because of the
attenuation in branch layer.
5.3.2.3
Contribution of trunks
The trunk-related backscattering varies with both trunk size and density. Trunk size is
determined by DBH and trunk height, and the trunk density is same as the stand density
in the forest. In contrast with the contribution of leaves and branches, the trunk-soil
double bounce is much stronger than trunk volume scattering.
The trunk-soil interaction and trunk volume scattering are sensitive to the variation of
DBH (Figure 5-6e). The difference of the highest and lowest values in trunk-soil
interaction is as high as 9dB. The difference in trunk volume scattering is only 3dB, but
the curve is more complicated. The leaf and branch volume scattering is not affected by
the variation o f DBH. The scattering in other components decreases almost linearly. In
accordance with the curve of trunk-soil interaction, the total scattering increases slowly,
then decreases after DBH > 0.16m. The change in total scattering is 2.6dB.
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As shown in Figure 5-6f, when stand density (# of trees / m^) increases, the trunk-soil
interaction increases, then decreases after stand density > 0.08. The possible reason is
that, with denser trees, there is less space for double bounce between trunks and soil
surface. The decrease is around 3dB in the range of stand density in Figure 5-6f. On the
other hand, the trunk volume backscattering increases up to 5dB. It is obvious that there
is more chance o f trunk volume backscattering at higher stand density. The leaf and
branch volume scattering is not affected by the variation o f stand density. The scattering
in other components decreases almost linearly. With the compensation of inereased trunk
volume scattering, the total backscattering decreases only IdB.
It was found in ground-measured data that trunk height was almost linearly correlated
with tree height (R^=0.81). Here the backscattering is simulated with the variation of tree
height, which affects the heights of three layers (leaf canopy, leaf+branch, and trunk).
These variations in turn result in different contribution from leaves and branches. In
Figure 5-6g, the scattering from all components (except leaf volume scattering) is
tremendous. When the tree height increases from 6m to 24m, the branch volume
scattering, the component that has highest contribution to total scattering, increases up to
6dB. The trunk volume scattering increases slowly, then drops 7dB. The trunk-soil
double bounce drops more than 40dB. The soil surface backscattering, leaf-soil and
branch-soil interaction even reaches infinity (-80 dB in the model) when the tree is higher
than 18m. With the compensation of decreased scattering from these components, the
total backscattering increases 2dB.
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5,3.3
Model validation
Field measurements were made in January 20-27, 2002 during the second field trip, one
week earlier than the JERS-1 SAR data acquisition. Among the 32 study sites visited,
there were 9 dry dipterocarp forests, 9 mixed deciduous, 5 pine transition, 6 dry
evergreen, and 3 moist evergreen. With a point-quadrant plot method, the biophysical
attributes at each site were measured; tree height, trunk height, DBH, stand density. The
forest fractional cover and Zyl/values were estimated with JERS-1 VNIR imagery. The
microwave/optical synergistic canopy scattering model is validated with these ground
measurements and JERS-1 SAR data.
In addition to the ground-measured biophysical attributes, other inputs of the model are
listed in Table 5-3. The values of some parameters be different in each forest type. The
leaf and branch radii and soil conditions are selected based on the experiences in
fieldwork. The dielectric constants (s) of forest components are calculated based on
vegetation moisture and local temperature with the model described in Ulaby et al.
(1990). Only backscatter in L-band with incidence angle of 35° and incidence azimuth
angle of 278° is modeled to be consistent with JERS SAR system.
As shown in Figure 5-7, there are logarithmic relationships between modeled
backscattering (LHH) and ground-measured biophysical parameters. The modeled
backscattering increases rapidly and then reaches the saturation point at LAI = 4 (Figure
5-7a). The increments of modeled backscattering with tree height (Figme 5-7b), trunk
height (Figure 5-7c), and DBH (Figure 5-7d) are also observable, but the residuals are
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higher because of the attenuation from leaves. The relationship between modeled
backscattering and stand density is very weak (Figure 5-7e). One possible reason is that
stand density is not a good indicator of forest growth. A young secondary forest could
have similar stand density as natural undisturbed forest. In late succession stage, the stand
density of natural forest declines, a similar phenomenon as the degraded forests disturbed
by human and natural activities (Aber and Melillo 2001).
The modeled backscattering coefficients are compared with JERS-1 SAR measurements.
In Figure 5-8a, all points were scattered along the 1:1 line, indicating that the model fits
well with SAR data. The root mean square error (RMSB) of the model simulation can be
calculated as:
M
\2
\'Z (^SA R -(^L ieiy
RMSE = \ ^ -------------------\
M
(5-21)
where M is the number o f study sites that were used for validation. The RMSE in this
study was 0.94dB in comparison with the JERS-1 SAR observed and modeled
backscattering coefficients at the 32 study sites. As shown in the residual plot in Figure 58b, most o f the residuals scatter between ±ldB. The only site with residual higher than
2dB is teak plantation in the study area, whose regular pattern results in very high
backscattering in JERS-1 observation. Figure 5-8 indicates that the synergistic canopy
scattering m o d el cou ld b e u sed to predict the backscattering in th e stu d y area at an
accuracy of around IdB.
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5.4
Model Inversion and Forest Parameters Estimation
5.4.1 Model inversion
In the microwave/optical forest scattering model, the backscattering coefficient is
determined by several forest structural parameters: leaf size, leaf density (#/ha), dielectric
constants
, heights of each layer, radii of branches and trunks, and
density o f branches and trunks (#/ha).
_ size, leaf _ density,
branchf trunk,fflayerl^tayen.f^layer3 ’
branch _ radius, branch _ density, DBH, 5 tan (/ _ density)
The forest structures are unique in each forest type. In accordance with the forest type
map created from optical remotely sensed data, only three major forest types are
considered in this chapter: dry dipterocarps, mixed deciduous, and tropicalevergreen
forests. Table 5-4 lists a series of parameters that are applied in themodel for each forest
type. As described in Chapter 2, some of the forest structural parameters are highly
correlated to each other. Figure 5-9 is the scatterplots of ground-measured stem height vs.
tree height and DBH vs. tree height. The regression equations are:
stem H = 0.559* tree i f -1.328
(5-23)
^
^
DBH = 1.332* tree _ H + 4.29
Here the units of stem_H and tree H are meters while the DBH is in centimeters.
The leaf density can be calculated with Eq.5-15 and 5-16 from optical remotely sensed
data. With the relationships in Table 5-4 and Eq.5-23, the model only needs two input
parameters: tree height and forest stand density. Then Eq.5-22 becomes:
cr^de/ = f {tree _ H , sta n d _density)
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(5-24)
For each pixel in the JERS-1 SAR data, a least-square-error optimization technique was
applied to estimate these two forest structural parameters with model inversion. The
standard criteria listed below:
The initial values of tree H and standjdensity for each forest type were given at the first
iteration. At each iteration, a new
was generated and compared with
1) at each pixel. The model stopped when the derivative of
(JERS-
between two iterations
was less than 10'® or the iteration numbers exceeded lO'*. Then the tree H snd
standjdensity at that iteration were outputted.
5.4.2
Forest structural parameters by model inversion
Figure 5-10 is the modeled tree height distribution in Mae Chaem Watershed. The non­
forest areas as shown in the forest type map (Figure 2-4) are masked out in Figure 5-10
and the following figures. Most of the trees in the watershed are higher than 10 meters.
Most of the dry dipterocarps are lower than 15 meters. Only some isolated dry
dipterocarps are around 20 meters. The mixed deciduous forests have a wider range
between 15 to 30 meters and the distribution is more heterogeneous than dry dipterocarp
forests. There are no trees higher than 30 meters in dry dipterocarps and mixed deciduous
forests. The tropical evergreen forests have a very wide range changing from 15 meters to
higher than 3 0 m eters. T here are also so m e isolated areas w ith tree h eig h ts around 12
meters.
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Figure 5-11 is the modeled forest stand density distribution in the watershed. In dry
dipterocarps and mixed deciduous forests, there is a wide variety of stand density ranging
from 100 to 600 trees/ha and higher. The stand density of dry dipterocarps is much higher
than mixed deciduous. In accordance with the ground-measured parameters, the stand
density is negatively related to tree height. When the tree is higher than 15 meter, the
stand density in this area is around 100-400 trees/ha. When the tree is lower than 15
meter, the stand density could reach 600 trees/ha or higher. Opposite with the tree height
map, there is a trend of decreasing stand density from east to west in mixed deciduous
forests. The model does not work for tropical evergreen forests. The modeled stand
density distribution saturates at around 500 trees/ha.
5.4.3
Uncertainty analysis
The error e of the model inversion is defined as the absolute difference between modeled
and JERS-1 SAR observed backscattering coefficients. It is in the imit of dB:
_
0
* ~ Pm ode;
(5-24)
^SA R
In Figure 5-12, most of the areas in dry dipterocarp forests in the watershed have errors
less than IdB, indicating that the forest structural parameters estimation with model
inversion works well in these forests. In the mixed deciduous forests where the
topographic relief is high, the error could reach 2-3 dB. In some areas on the tops of
mountains, mostly in tropical evergreen forests where the elevation is very high and slope
is very steep, the error is even higher than 4dB. In these areas, the tree height and stand
density are highly overestimated or underestimated. As shown in Figure 5-5b, the
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topographic correction of the SAR image did not work well in these areas, which
introduces high errors from topographic effects.
The modeled tree height and stand density were also compared with ground-measured
values at the study sites. The average of the model inversion error at all study sites was
only 0.44dB although the individual values range from 0 to 3.26 dB. In Figure 5-13a, the
modeled and measured tree height values at most of the study sites seattered along the 1:1
line. There was obvious overestimation in young forests in which the trees were shorter.
These forests were mostly dry dipterocarps in which the measured trees were between 510 meters whereas the modeled ones were 10-15 meters. For forests with higher trees, the
modeled and the measured values fit well.
There is one outlier in Figure 5-13a with low value of measured tree height (13.18 meter)
but high value of modeled tree height (28.0 meter). It is a degraded and recovered
tropical dry evergreen forest with high heterogeneity at the study site (S2-14). The
ground measurements could he xmderestimated using point-quadrant plot method as
discussed in Chapter 2. On the other hand, the canopy scattering model did not account
the scattering contribution from the interaction between young (short) and old (tall) trees
at this site. As a result, a higher tree height was introduced to mateh the JERS-1 SAR
observed scattering eoefficients. The error of model inversion at this study site is only
0.19dB.
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The measured and modeled stand density at the study sites also scattered along the 1:1
line (Figure 5-13b). The modeled stand density at most of the tropical evergreen sites,
including the outlier (S2-14) in Figure 5-12, saturate at about 500 trees/ha. For other
forests (mostly dry dipterocarps and mixed deciduous), the modeled and measured stand
density fit well.
For each forest type, the average and standard deviation of modeled tree height, stand
density and the error of model inversion at all study sites were listed in Table 5-5. The
modeled tree height increased from dry dipterocarps, mixed deciduous, pine transition, to
tropical evergreen forests. The pine transition had the highest standard deviation.
However, in the forest type map, the pine transition, dry evergreen, and moist evergreen
forests were combined as tropical evergreen forests. In this way, the standard deviation is
only 2.93. In accordance with ground measurements, there is a negative relationship
between tree height and stand density. Young secondary dry dipterocarps have higher
density, while the tropical evergreen forests with less human disturbance have lower
density. The standard deviation of moist evergreen forests is very low (0.58) because of
the saturation of model inversion in these forests. Since most study sites were chosen at
relatively flat areas, the topographic effect at these sites was minimal and the average
errors of model inversion at all forest types were less than IdB. The pine transition had
the highest standard deviation that leaded to a model inversion error of around IdB in
tropical evergreen forests. The model inversion in dry dipterocarps and mixed deciduous
forests worked very well.
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In Figure 5-14, the average values of modeled tree height (Figure 5-14a) and stand
density (Figure 5-14b) were compared with the ground-measured values in each forest
types. The modeled tree heights were generally overestimated. The overestimation was
more obvious in dry dipterocarps and moist evergreen forests, and less obvious in mixed
deciduous, pine transition, and dry evergreen forests. The total root-mean-square-error
(RMSE) in tree height estimation was 4.6 meter in all study sites, and 3.8 meter when 8214 was removed. The modeled stand densities in dry dipterocarps and pine transition fit
well with groimd-measured values. But they were overestimated in mixed deciduous and
dry evergreen forests, and were underestimated in moist evergreen forests because of the
saturation in model inversion. The total RMSE in stand density estimation was 300 #/ha
in all study sites, and 299 trees/ha when S2-14 was removed.
5.5
Conclusions and Discussion
In this chapter, a microwave/optical synergistic radiative transfer model was built to
simulate the radar scattering from forests in the study area. The backscattering
coefficients o f the forest components, such as branches and trunks, were modeled. The
leaf scattering and its attenuation to the woody components were quantified in the model
with leaf area index retrieved from optical remotely sensed data using the method in
Chapter 4. The root-mean-square error (RMSE) of the model was 0.94 dB when
compared with JERS-1 SAR backscattering coefficients.
Two forest structural parameters, tree height and stand density, were estimated by model
inversion with least-square optimization techniques and JERS-1 SAR and VNIR data.
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The error of model inversion was less than 1 dB in most of the areas in the watershed.
However, in the areas with high relief and steep slopes, the model inversion error could
be higher than 4 dB, which introduced high error in the estimation of forest structural
parameters. When all study sites were considered, the total root-mean-square error of tree
height estimation was 4.6 meter. The total root-mean-square error of stand density
estimation was 300 trees/ha. The average model inversion error was 0.44 dB although the
individual values ranged from 0 to 3.26 dB.
The model worked well for the estimation of structural parameters in dry dipterocarps
and mixed deciduous forests. Most of the dry dipterocarps were lower than 15 meters.
The mixed deciduous forests had a wider range between 15 to 30 meters and the
distribution was more heterogeneous. The stand density in these forests varied from 100
to 600 trees/ha or higher. In accordance with the ground measurements, the tree height
was negatively related to the stand density in these forests. The error of model inversion
was less than IdB in most of these forest areas. The modeled tree height in tropical
evergreen forests ranged from 15 to 30 meters and higher. The model inversion to
estimate stand density did not work well in these forests. Its distribution saturated at
approximately 500 trees/ha.
In mormtainous areas, the estimation of forest structures with model inversion was highly
affected by the quality of SAR imagery and the accuracy of topographic correction. The
evergreen forests, especially moist evergreen forests, are distributed on the top of
mountains with high relief. It was difficult to correct the topographical effects to JERS-1
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SAR data in these areas. The error of model inversion in tropical evergreen forests was
higher than that in other forests. In some areas when the relief was very high, the
topographic effect cannot be corrected. The errors of model inversion in these areas were
higher than 4dB, which introduced high uncertainty in the estimation of forest structural
parameters in these areas. A more accurate DEM data and a better correction algorithm
technique for the topographic correction to SAR imagery will be studied in the future.
The dense leaf cover in tropical forests is a big obstacle in SAR remote sensing. It highly
attenuates the scattering from woody components like trunks, branches, and their
interaction with soil surface. The model developed in this study took the advantage of
leaf properties quantified with optical remotely sensed data. As a result, the modeled
backscattering was more sensitive to woody parameters in tropical forests. Therefore, this
model can also he used for the woody biomass estimation in tropical forests which is
discussed in next chapter.
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5.6
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texture information in the estimation of northem deciduous and mixed wood
forest leaf area index (LAI). Remote Sensing of Environment: 64, 65-76.
131
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Table 5-1
System parameters of JERS-1 SAR and NVIR sensors.
JERS-1 satellite
Altitude:
~570km
Orbit:
97.67°, sun-synchronous
Orbital direction: Descending
SAR
VNIR
L-band (1.275 GHz)
Spectral frequency
Bandl: 0.52 - 0.60 pm (green)
Band2: 0.63 - 0.69 pm (red)
Band3: 0.76 - 0.86 pm (NCR.)
Incident angle (right-sided):
IFOV:
View angle
21.3 (nadir viewing)
Near-range: 35°
Far-range: 42°
Polarization
HH
/
12.5m
18m
Pixel size
Swath width
75km
75km
# ofLooks
3
/
03/06/1998
Acquisition date
02/27/1998
Table 5-2
Input parameters for model simulation.
parameter
range
Leaf size (m)
Vi long axis
0.02 ~ 0.07
elliptic ratio
1 .2 - 1.5
Branch size (m)
radius
0.016-0.047
Trunk size (m)
radius
0.06 - 0.22
LAI
0 .2 -8 .0
Branch density (#/m^)
0 .5 -3 .0
Tree density (#/m^)
0 .0 3 -0 .1 8
Tree height (m)
6 -2 4
mean
0.05
1.5
0.031
0.1
1.0
1.5
0.1
10
132
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Table 5-3
Input parameters
measurements
dry
dipterocarps
Leaf radii
(0.07,0.05)
(m) (ai,a2)
Branch
0.022
radius (m)
for model validation (in addition to groimd
mixed
deciduous
pine
transition
(0.05,0.033)
dry
evergreen
moist
evergreen
(0.03,0.02)
0.029
0.031
(22.29,-7.26)
(28.23,-8.95)
Sleaf
(16.94,-5.7)
^branch
(19.7,-6.53)
^trunk
(13.57,-4.66)
Ssoil
(5.3,-0.65)
Soil
roughness
Root-mean-square height (m): 0.015
Correlation length (m): 0.15
0.033
(35.23,10.78)
(34.45,10.49)
(26.53,8.41)
(10.23,1.95)
dry dipterocarps
mixed deciduous
(7, 5, 0.5)
(5, 3.3, 0.5)
tropical
evergreen
(4, 3, 0.7)
^ le a f
(16.94,-5.7)
(22.29,-7.26)
(28.23,-8.95)
^ soil
(5.3,-0.65)
(5.3,-0.65)
(10.23,-1.95)
p
(19.7,-6.53)
(19.7,-6.53)
(26.53,-8.41)
(13.57,-4.66)
(13.57,-4.66)
(23.26,-7.53)
(tree_Hstem_H)*2/3
(treeH stem H)* 1/3
Stem H
DBH/4
Stand density* 8
(tree_H-stem_H)2*/3
(treeH stem H)/3
(tree Hstem H)*2/3
Stem H
DBH/4
Stand_density*32
Leaf 3-D size
(cm)
branch
^ trunk
Height of layer 1
Height of layer 2
Height o f layer3
Branch radius
Branch density
(tree_H-stem_H)* 1/3
Stem H
DBH/4
Stand_density*16
133
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Table 5-5
Dry dipt
Mixed dec
Pine trans
Dry ever
Moist ever
Average and standard deviation of modeled tree height, stand density, and
tree H
(m)
stddev
14.34
2.35
14.65
0.58
16.73
5.93
15.65
2.45
25.99
0.41
density
(#/ha)
701.11
677.33
747.80
639.80
499.33
stddev
328.05
313.01
342.98
313.16
0.58
error
(dB)
0.13
0.18
0.76
0.74
0.50
stdev
0.40
0.46
1.41
0.66
0.86
134
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Figure 5-1
Geometry of a forest canopy.
1.4
trunk
leaf
branch
u.
0
20
40
60
80
100
120
140
160
180
inclination angle
Figure 5-2
PDF functions of leaves, branches, and trunks applied in the synergistic
canopy scatter model.
135
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1 -
-
'
I" ** ’
«n
?k*
;
W i ' *'i
^
> « iJL^jV
- 'X l f l f -
JJU r
Figure 5-3
JERS-1 SAR (a) and VNIR data (NIR+Red+Green) (b) in the study area.
136
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Figure 5-4
Figure 5-5
DEM data (a) and geometrically corrected JERS-1 SAR image (b).
Local incidence angle image (a) and the topographically corrected image
(h). The layovers and shadows are the white areas in (a).
137
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1
1
1
1
1
1
1
-10
(a)
I
(b)
-20
-25 /
1
-10
T
>
O
a1=0.07
a1=0.06
— a1=0.05
--a 1 = 0 .0 4
-■--a1=0.03
— a1=0.02
I
-10.5
1.2
1.25
1.3
1.35
1.45
1.4
s
------ total
— soil
— leaf
------ leaf-soil
........branch
-----branch-soil
----- trunk
........trunk-soil
-30
-35
^0
-45
1.5
4
LAI
leaf elliptic ratio (a1/a2)
(c)
total
— soil
— leaf
leaf-soil
brancti
brancti-soll
trunk
trunk-soil
CD -25
total
soil
leaf
leaf-soil
branch
branch-soil
trunk
trunk-soil
1
0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05
m
•D
O
-5
-5
-10
-10
(e)
-15
T<
>u
o
E
2.5
I. -20
-25
-total
- soli
- leaf
-leaf-soil
- branch
branch-soil
- trunk
-trunk-soil
-30
-35
-40
-45
0.06
0.08
0.1
0.12
0.14
0.16 0.18
0.2
(g)
S ' -20
M -40
— total
— soil
— leaf
leaf-soil
branch
— branch-soil
— trunk
trunk-soil
-60
10
15
total
— soil
— leaf
leaf-soil
— branch
branch-soil
trunk
trunk-soil
-25
-35
^0
0.05
0.075
0.1
0.125
0.15
0.175
Stand density (*Vm2)
D B H /2 (m )
E
2
o
-20
E
cn
1.5
branch d en sity (#/m 2)
bran ch rad iu s (m)
Figure 5-6
Modeled
backscattering of different
forest components: leaf size
(a); LAI (b); Branch radius
(c); Branch density (d); DBH
(e); Stand density (f); Tree
height (g).
20
tre e h eight (m)
138
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£ -10
10
15
20
tree height (m)
-2 -
-2 -
CQ
5,
o
E
(d)
-4 s
-6 -
D>
W -8
■o
o
■0a) -10
o
E
(c)
-4 -
-12
o -6E
o
>
« -8 ■
o
o
-
"
•
♦
*
*
I
I
♦
♦
-14 - ................... 1....................................... 1 .................
5
10
15
20
*
* *
t
^ *
*
*
*
*
*
*
*
-12 -
♦
*
-14 -1
♦
1
10
1
20
.... ...........T" ....... ....■
!
30
40
50
DBH (cm)
stem height (m)
500
1000
1500
stand density (#/ha)
Figure 5-7
Backscattering simulation based on ground-measured forest parameters:
LAI (a), tree height (b), trunk height (c), DBH (d), and stand density (e).
139
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m
T3
O
E
O)
w
■a
o
0)
■o
o
E
-13
-14
-10
-12
8
JERS-1 sigmO (dB)
3
2
1
(0
3
5 0W
9>
1
■2
■3
-14
-10
-12
■8
modeled sIgmO (dB)
Figure 5-8
Comparison of JERS-1 SAR observed and modeled backscattering
coefficients: scatterplot of backscattering coefficients (a) and residuals (b). The 1:1 line is
also drawn in (a).
140
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40
X stem_H
35
E
r2 = 0.72
= 0.84
□ DBH
30
X
ffl 25
Q
■D
C
re 20
?
X
15
□□
I
E
o> 10
</)
5
0
0
5
10
15
20
25
30
tree_H (m)
Figure 5-9
Scatterplot of stem height and DBH to tree height measured at study sites.
TreeH
I
Figure 5-10
Modeled tree height distribution in the watershed.
141
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Density
I
I
Figure 5-11
Modeled stand density distribution in the watershed.
error (dB)
Figure 5-12
Error distribution of model inversion.
142
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?
25
♦♦
TJ
RMSE=4.6 meter (with outlier)
RMSE=3.8 meter (without outlier)
0
5
10
15
20
25
30
measured tree height (m)
(a)
2000
RMSE=320 #/ha (with outlier)
RMSE=319 #/ha (without outlier)
^ 1500
0
500
1000
1500
2000
m easured stand density (#/ha)
(b)
Figure 5-13
Scatterplots of modeled and measured tree height (a) and stand density (b)
at all study sites.
143
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30
25
^
20
I
15
n measured
0 modeled
sUrn 10
5
0
dry
dipt
mixed
pine
dry
trans ever
moist
ever
(a)
1000
(0
£
800
□ measured
600
(A
C
V
•D
■o
c
nj
(/)
□ modeled
400
200
0
dry
dipt
mixed
pine
trans
dry
ever
moist
ever
(b)
Figure 5-14
Comparison of modeled and measured tree height (a) and stand density (b)
in different forest types.
144
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Chapter 6
Aboveground Woody Biomass Estimation with Microwave and
Optical Remotely Sensed Data
6.1
Introduction
Most of the world’s terrestrial biomass (80-92%) is contained in forest ecosystems
(Beaudoin et al. 1994). Aboveground forest biomass, or so-called biomass density, is of
great importance in understanding the state and dynamics of ecosystems and tbeir
interaction with global carbon cycles/climate change studies because of its ability to lock
up carbon and its potential for carbon exchange with the atmosphere through forest
destruction and regrowtb (Luckman et al. 1998). In this study, aboveground biomass
represents the total woody biomass of trees of diameter 10cm or larger, including twigs,
branches, trunks, and bark (Brown and Lugo, 1992). The amount of green leaves could be
represented by LAI, which has been deeply studied with optical remote sensing
techniques (Peterson et al. 1987; Baret and Guyot 1991; Qi et al. 1995, 2000; Turner et
al. 1999).
In the past decades, forest biomass mapping from SAR data has become one of the most
promising applications of radar remote sensing to vegetation studies. A number of studies
have been done to estimate aboveground forest biomass using SAR remotely sensed data.
One of the most common approaches is to empirically relate SAR backscattering
intensity to biomass with ground measurements. Le Toan et al. (1992) found that there
145
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was a good correlation between SAR intensity and forest biomass for lower frequency
such as P- and L-band data. C-band data primarily interacted with the crown layer
(Saatchi and McDonald 1997) and rapid saturation of the C-band backscattering
coefficient occurred with the increase in forest biomass (Dobson et al. 1992). Cross
polarized backscatter (HV) was found to have the highest correlation with forest biomass.
HH is related to both trunk and crown biomass, whereas VV and particularly HV returns
are linked to crown biomass (Beaudoin et al. 1994).
Luckman et al. (1997) found that lower frequency (L-band) SAR imagery could be used
to discriminate different levels of forest biomass up to 110 ton/ha. With the effect of
topography, the biomass threshold, above which there appears to be no further increase in
L-band radar backscatter, was around 60 tons per hectare (t/ha), and the error is estimated
to be around 30% of the biomass value (Luckman 1998). Other studies (Le Toan et al.
1992, Dobson et al. 1992) using P- and L-band data in temperate forests indicated that it
was possible to estimate biomass up to 200 ton/ha. With sensors at very long wavelengths
(>lm) such as VHF SAR data, the backscattering coefficient is strongly correlated to
characteristics of the tree trunk layer and the signal saturation is not observed up to 360
tons/ha (Melon et al. 2001).
However, the empirical models did not add any physical explanation of the processes that
drive the changes in backscattering. The biomass retrieval by means of relating biomass
to SAR backscattering is shown to be ill-posed (Dobson et al. 1995). These relationships
are highly affected by the structural properties of the forest such as species, age, height.
146
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diameter, and tree component orientation distributions (Dobson et al. 1995), and other
factors as topography, soil moisture, history, and local disturbances. It is also problematic
when expanding the measured commercial biomass to the total aboveground biomass.
Several canopy scattering models were successfully developed to simulate radar
backscattering in temperate forests when these structural parameters were taken into
account (Karam et al. 1995; Ulaby et al. 1990; Sim et al. 1991). These models are a
theoretical approximation of forest scattering based on the first or second resolution of
radiative transfer functions (RTF). Among these models, the soil surface was simply
assumed as a continuous and slightly rough dielectric surface. In forests with higher
biomass, the soil backscattering is low and this simplification is acceptable. However, in
sparse forests such as highly degraded or young secondary forests, the contribution of soil
backscattering is under-estimated which introduces high error in biomass estimation.
Due to the complicated natures of the input parameters, it is not a straightforward task to
develop an effective inversion algorithm of these physical models for the purpose of
biomass retrieval. Pierce et al. (1994) applied an artificial neural network to fulfill the
model inversion of forest biophysical attributes. Ranson et al. (1997) used a combination
of forest succession model and a canopy scattering model to develop the relationships
between biomass and backscattering coefficients. The approach produced similar results
with ground measurements and observed SAR data when aboveground biomass is less
than 150 ton/ha, in the same level of direct empirical biomass retrieval.
147
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When applied to SAR data, both the empirical and microwave RTF model-based biomass
retrieval approaches only work on forest with sparse to medium biomass such as
temperate coniferous or broadleaf forests. In dense tropical forests, because of the strong
attenuation from the leaf layer, the radar backscattering is no longer sensitive to biomass
when the forest reaches the threshold biomass level. If the contribution of leaves to the
total backscattering can be quantified, the accuracy of woody biomass estimation could
be improved at a much higher level.
Optical remotely sensed data is mostly sensitive to the green leaf properties of the forest
canopy. In optical remote sensing, the reflected energy of the Earth surface is determined
by surface physical properties such as strong chlorophyll absorption and low reflectance
(and transmittance) in visible bands, and high reflectance due to internal leaf structures in
near infrared (NIR). Optical remote sensing has been widely used to derive information
o f vegetation properties such as fractional cover and green leaf area index (Jasinski and
Eagleson 1990; Qi et al. 2000). A synergistic use of optical and microwave remote
sensing could quantitatively evaluate the effect of leaf canopy to SAR backscattering and,
therefore, could enhance the availability of biomass estimation.
In this chapter, several methods were used to estimate aboveground biomass in the study
area. Firstly, a simple regression model was built from the relationship between JERS-1
SAR backscattering coefficients and ground-measured biomass. Then, with the forest
structural parameters estimated in Chapter 5, the allometric equations were also applied
to calculate biomass distribution in the study area. Finally, the backscattering
148
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contribution o f leaves and their attenuation effects to other forest components were
calculated based on both JERS-1 VNIR data and the microwave/optical synergistic model
built in Chapter 3. The modeled backscattering with leaf compensation was then
compared with ground measurements to estimation biomass distribution in the study area.
6.2
Biomass Estimation with a Simple Regression Method
In the past studies, a simple cr° -biomass regression model was often built to estimate
biomass with SAR data (Luckman et al. 1997, Hashim and Kadir 1999, Santos, et al.
2003). In this section, the cr° in JERS-I SAR image at each study site was retrieved and
the <T° -biomass at all study sites was plotted (Figure 6-1). It was expressed as:
^°jERs-i = -13.838 + 0.963 * \n{biomass - 5.487)
(6-1)
Here the biomass is in the unit of ton/ha. The cr° was logarithmically related to biomass.
At degree of freedom = 29 (32 study sites - 3 model coefficients), the
of curve fitting
was 29.15, much smaller than the critical 2^0.05,29 (46.19) at confidence level of 95%,
indicating that the curve fitting in Figure 6-1 was valid and the <7° and biomass were
logarithmically related.
With Eq.6-1, the biomass distribution in the study area can be estimated with JERS-1
data (Figure 6-2). Unlike the forest fractional cover and leaf area index distribution, the
woody biomass distribution estimated in this regression model did not agree with the
forest type distribution. In the east of the watershed is Chiang Mai city and the forests are
inevitably disturbed by human activities at a higher intensity. The Mae Chaem Town is
149
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the only major human settlements in the middle and east of the watershed and the forests
are less disturbed. This could be the possible reason that there had higher biomass
distribution in the west than that in the east. However, in lack of a second set of ground
measurements, it was impossible to validate this map. Moreover, the west part is in the
near-range o f JERS-1 SAR sensor whereas the east part is in far-range. Although the
JERS-1 SAR data applied in this study has been calibrated and the pixels have been
changed from slant resolution to ground resolution, the mis-calibration in this high
mountainous area could also be a possible reason for the east-west variation of biomass
distribution.
6.3
Biomass Estimation with Compensation of leaf Attenuation
With the leaf area index {LAI) retrieved from JERS-1 VNIR data, the attenuation effect of
leaf layer on other forest components could be quantified in the micro wave/optical
synergistic canopy scattering model. Figure 6-3 is the
image created using the
method described in Chapter 5. The non-forest open areas are masked out in the analysis.
Since the JERS-1 data was acquired in the dry season, the dry dipterocarps and mixed
deciduous forests had low Zyf/values (around 1.0 or lower) while the evergreen forests
had high Zyl/values (4.0 or higher).
A microwave/optical canopy scattering model was developed in Chapter 5 to simulate the
backscattering from tropical forests. As shown in Figure 6-4a, there is a logarithm tic
relationship between biomass and total modeled backscattering coefficients at the study
sites. The modeled total backscattering scattering increases rapidly with biomass, then
150
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quickly reaches the saturation point at biomass around 100 ton/ha, consistent with the
past studies (Le Toan et al. 1992, Dobson et al. 1992, Luckman et al. 1997).
The backscattering from each component of forests, however, changes differently with
the increase of biomass (Figure 6-4b). In L-band HH polarization, the backscattering
from branches plays the most important role and its variation with biomass is similar with
the total backscattering. Trunk-ground double bounce is the seeond important
backscattering component when biomass is lower. It decreases rapidly with higher
biomass and reaches infinity (-80dB in the model) when biomass > 400 ton/ha. Leaf
volume scattering also increases with biomass, but the residual is very high because leaf
amount is not directly related to woody biomass, especially in the dry season in the study
area. Branch-ground interaction, trunk volume scattering, and leaf volume scattering also
deereases with higher biomass but with a lower rate than that of the trunk-ground
interaction.
With the mierowave/optical canopy scattering model and the tree height map developed
in Chapter 5, the attenuation factor, x, at each study site could be calculated from Eq.5-13
to 5-15 when LAI-wus known. The attenuation from leaf layer can also be compensated
and the scattering from the woody components (branches and trunks) be modeled. The
leaf-compensated backscattering also had a logarithmic relationship with biomass. Figure
6-5 showed the logarithmic regression between biomass and JERS-1 SAR measured,
modeled, and modeled after attenuation compensation in L-band and HH polarization
(same system parameters as JERS-1 SAR). The JERS-1 backscattering increases quickly
151
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with biomass, then it slows down at biomass of around 100 ton/ha and tends to saturate.
The modeled backscattering is about IdB lower than JERS-1, but the trend with biomass
is similar. The modeled backscattering with leaf attenuation compensation is more
sensitive to biomass. With limited study sites, the increase of backscattering does not
slow down until biomass reaches 200 ton/ba. There is no obvious saturation in the
modeled backscattering (no-leaf) in Figure 6-5.
At lower biomass, the green leaves have significant contribution to the total
backscattering and, therefore, the modeled backscattering is higher than the one after leaf
attenuation compensation. When biomass is higher than 300 ton/ba, the leaf attenuation
to the woody components (branches, trunks) becomes significant and therefore, the
modeled backscattering is lower than the one after leaf attenuation compensation.
The coefficients o f these curve fittings and tbeir statistical tests are listed in Table 6-1.
For a chi-square test, with degree of ffeedom=29 (32 - 3 coefficients in the model), the
critical value, ^'o.os,29> 46.19 at 95% confidence level. The
Table 6-1 are much smaller than z L s ,29
valid in Figure 6-5. The
values of ( t L i s
values of
and
therefore, the curve fitting lines are
very close to the z L
,29
■One
possible reason is that when the leaf attenuation is compensated, the topographic effect
on the SAR backscattering and its interaction with ground surface becomes more
significant, which introduces higher uncertainty to
.
152
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Figure 6-6 is the leaf scattering (Figure 6-4a) and leaf attenuation (Figure 6-4b) images in
the study area in the unit of dB. The leaf scattering component
is the combined
intensity o f leaf volume scattering and leaf-ground interaction. The leaf attenuation factor
(t) is the attenuation to the radar signals reaching or scattering from the branches, trunks,
and soil ground. The leaf scattering ranged from -32 dB in dry dipterocarps to -10 dB in
tropical evergreen forests. The leaf attenuation (x) ranged from 0.99 in dry dipterocarps
to 0.19 in tropical evergreen forests. The leaf scattering and its attenuation effects are
positively correlated to
values. When LAI increased in the study areas, both the leaf
scattering and its attenuation to woody structures increased. As a result, the value of
attenuation factor (x) was lower. Dry dipterocarp forests have lowest leaf scattering and
highest attenuation factor (x) values. In mixed deciduous forests, the leaf scattering is
higher and x is lower. Tropical evergreen forests have highest leaf scattering and lowest x
values.
With JERS-1 SAR data, L J/d ata, and the microwave/optical synergistic canopy
scattering model, the leaf scattering and its attenuation to woody forests could be
calculated. The scattering coefficient (
)
from woody forests (branches, trunks) is
then quantified by subtracting leaf scattering (
)
from the total backscattering (
)
then compensated for the two-way leaf attenuation (x). It is the combined contribution of
volume scattering from branches and trunks and their interaction with ground surface. In
power unit,
could be expressed as:
_
^SA R
^ w oody “ ■
^ le a f
X
,(■
2
153
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The woody scattering distribution in the study area was shown in Figure 6-7. It is obvious
that after leaf attenuation compensation, the radar scattering from woody forests
(branches and trunks) is more significantly influenced by topography in the study area.
Tropical evergreen forests have higher scattering from woody structures, but the values
vary greatly because o f the topographic effect.
With the regression model described in Figure 6-5 and Table 6-1, the biomass
distribution was estimated with the relationship between the woody forest scattering and
biomass (Figure 6-8). The topographic effect in tropical evergreen forests was
overwhelming so that the estimated biomass in these forests was even lower than dry
dipterocarps and mixed deciduous forests. From both Figure 6-2 and Figure 6-8, the
regression models could not successfully estimate the biomass in the study area,
especially in tropical evergreen forests where the topographic effects were significant.
The results are either overestimated or underestimated in different forests. There was
always a trend of increasing biomass from west to east of the study area.
6.4
Biomass Estimation with Synergistic Model and Allometric
Equations
The modeled tree height and stand density distributions in the study area were described
in Chapter 5. A least-square optimization technique was applied in model inversion while
the leaf scattering and its attenuation to woody forests (branches, trunks) were quantified
with JERS-1 VNIR data. Also, as described in both Chapter 2 and Chapter 5, the
diameter at breast height (DBH) was linearly correlated with tree height. With the tree
154
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height distribution map in Chapter 5, the DBH distribution was modeled (Figure 6-9).
Similar as the tree height distribution, most of dry dipterocarps have lower DBH values
(around 15-20 centimeters). Mixed deciduous forests have a wider range of DBH values
between 15-40 centimeters. The tropical evergreen forests have the highest heterogeneity.
Some o f the areas in tropical evergreen forests could have DBH values larger than 45
centimeters while in some isolated areas they are smaller than 10 centimeters. There is
also a trend o f DBH values increasing from west to east of the watershed. The trend is not
obvious in other forest types.
From the modeled tree height, stand density, and DBH distributions, the aboveground
forest woody biomass in the watershed could be calculated with the allometric equations
described in Chapter 2. In Figure 6-10, the dry dipterocarp forests have the lowest
biomass (50-150 ton/ha). There are also some isolated areas with biomass less than 50
ton/ha. The biomass in the areas close to human settlements is lower than the ones far
away. Mixed deciduous forests have higher biomass ranging from 50 to 200 ton/ha. In
some isolated areas it could reach 400 ton/ha.
The biomass distribution in tropical evergreen forests has the highest heterogeneity than
any other forest types. The biomass values range from 50 ton/ha to more than 400 ton/ha.
In some areas the biomass estimation is saturated. In some isolated areas, the biomass
distribution is even less than 50 ton/ha. The biomass estimation in tropical evergreen
forests is questionable because of two reasons: first, tropical evergreen forests locate atop
of mountains in the study area. In some areas, the topographic relief is so high and the
155
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slopes are so steep that the topographic effect to the JERS-1 SAR image cannot be
corrected. It introduces high error of model inversion in the areas. Second, the modeled
stand density in tropical evergreen forests saturates at around 500 trees/ha (Chapter 5),
and therefore, the accuracy of the biomass estimation in these areas are lower than dry
dipterocarps and mixed deciduous forests.
The modeled biomass values at the study sites were compared with ground-measured
values (Figure 6-11). The modeled and measured biomass values scattered along the 1:1
line. The biomass values at most of the study sites were less than 200 ton/ha. Both the
modeled and measured biomass values at moist evergreen forests were higher than 350
ton/ha. There were one pine transition site and one dry evergreen site at which the
modeled biomass was underestimated. The site (S2-14) was an outlier at which the
modeled biomass was highly overestimated. This overestimation came from the model
saturation. The site was also an outlier in the scatterplot of modeled and measured tree
height at study sites in Chapter 5 (Figure 5-13a). There was no study site with biomass
between 200 to 350 ton/ha in both modeled and measured methods. The total root-meansquare error (RMSE) was 121 ton/ha with this outlier (S2-14), and 88 ton/ha when the
outlier was removed.
When compared in different forest types (Figure 6-12), the modeled biomass was
obviously overestimated at dry dipterocarps study sites because of the overestimation in
the modeled tree height at study sites. In mixed deciduous, pine transition, and dry
evergreen forests (when the outlier S2-14 was removed), the average values of modeled
156
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biomass fitted well with measured values. However, the standard error of modeled
biomass in pine transition was very high, indicating high heterogeneity in the zone. Even
when the outlier (S2-14) was removed, the standard error of dry evergreen was still
slightly higher than that of dry dipteorcarps and mixed deciduous. Both the modeled and
measured biomass values at moist evergreen forest sites were higher than 400 ton/ha. The
model tended to saturate and therefore the standard error was very low.
6.5
Conclusions and Discussions
In this chapter, several methods for biomass estimation with microwave and optical
remotely sensed data were examined. Firstly, a simple regression model was developed
with the observed JERS-1 SAR backscattering coefficients and ground-measured
biomass in the study sites. The model indicated low biomass in open areas and dry
dipterocarp forests close to the human settlements, and high biomass values in mixed
deciduous forests. However, due to the severe topographic effect to the JERS-1 SAR
data, the estimated biomass in tropical evergreen forests was low and varied greatly.
There was an obvious trend of increasing biomass from east to west of the study area.
The possible reason comes from the mis-calibration o f JERS-1 SAR sensor. With the
limited study sites and biomass measurements, it was impossible to validate the biomass
map derived with this method.
Secondly, with the JERS-1 VNIR optical data and the microwave/optical synergistic
canopy scattering model developed in Chapter 5, the volume scattering from leaf layer in
forests and its interaction with ground surface was quantified, and its attenuation factor
157
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(x) was calculated. Then the scattering from woody forests (branches, trunks) was
modeled by subtracting the modeled leaf scattering from the observed JERS-1 SAR
backscattering coefficients and compensating the attenuation from leaf layers. According
to the relationship between the woody scattering and biomass over the study sites, the
woody scattering was more sensitive to biomass when the attenuation from leaf layer was
compensated. A similar regression model was built based on the relationship between the
modeled woody scattering and ground-measured biomass over the study sites. It was
applied to estimate the biomass distribution in the study area. However, when the leaf
scattering was removed and its attenuation to the woody forests compensated, the
backscattering coefficients were more severely affected by topographic variation. The
modeled biomass also revealed a trend of increasing biomass from east to west of the
study area. Similarly, the biomass map in this method cannot be validated.
Thirdly, with the tree height and stand density distributions with the microwave/optical
synergistic canopy scattering model in Chapter 5, the DBH distribution was estimated
and the biomass was calculated with the allometric equations. The biomass map in this
method clearly showed the lower biomass distribution in dry dipterocarp forests (50-150
ton/ha, less than 100 ton/ha in most areas), medium distribution in mixed deeiduous
forests (50-200 ton/ha, greater than 100 ton/ha in most areas), and high biomass
distribution in tropical evergreen forests (50-500 ton/ha and higher). Tropical evergreen
forests showed a high heterogeneity in biomass distribution. It possessed the lowest
biomass in the areas with high relief and steep slopes where the topographic effects
cannot be corrected. As mentioned in Chapter 5, the stand density estimation with model
158
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inversion did not work in tropical evergreen forests and saturated at around 500 trees/ha.
Therefore, the biomass estimation in tropical evergreen forests tends to saturate.
The estimated biomass in the third method was compared with the ground-measured
biomass in all study sites. The root-mean-square error of the biomass estimation in the
study sites was 88 ton/ha. The biomass was overestimated in dry dipterocarp forests. The
model worked well in mixed deciduous forest. But the pine transition and dry evergreen
forests, which belong to tropical evergreen forests in the forest type map, had high
standard error, indicating high heterogeneity in these forests.
In conclusion, the synergistic use of microwave and optical remotely sensed data in a
canopy scattering model provided a new approach of biomass estimation in tropical
forests. However, this method is highly limited by the poor quality of SAR data and
geometric mismatch between SAR and DEM data. In mountainous areas where the
topographic effect to the SAR data cannot be corrected, the biomass estimation is very
questionable. The model could be applied to estimate forest biomass up to 200 ton/ha. In
forests with higher densities (biomass higher than 400 ton/ha), the SAR signal begins to
be saturated and is no longer sensitive to the biomass increments.
The accuracy of ground measurements is also critical to the validation of this method.
With limited study sites, the outlier of one site could result in 33 ton/ha o f RMSE of
biomass estimation in the area. Moreover, no study sites with biomass between 200 to
350 ton/ha were visited. As a result, it is impossible to validate the biomass estimation in
159
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this range. Since the ground-measured biomass at study sites with point-quadrant method
tend to be underestimated in heterogeneous forests, a better method for ground
measurements is needed in the future research.
160
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6.6
References
Baret F. and Guyot, G. (1991). Potentials and limits of vegetation indices for Lai and
APAR assessment. Remote Sensing of Environment: 35, 161-173.
Beaudoin, A., Le Toan, T., Goze, S., Nezry, E., Lopes, A. and Mougin, E. (1994).
Retrieval o f forest biomass from SAR data. International Journal of Remote
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Brown, S. and Lugo, A. E. (1992). Aboveground biomass estimates for tropical moist
forests of the Brazilian Amazon. INTERCIENCIA: 17(1), 8-18.
Brown, S., Gillespie, A. J. R. and Lugo, A. E. (1989). Biomass estimation methods for
tropical forests with applications to forest inventory data. Forest Science: 35(4),
881-902.
Dobson, M. C., Ulaby, F. T., Le Toan, T., Beaudoin, A. and Kasischke, E. S. (1992).
Dependence of radar backscatter on coniferous forest biomass. IEEE Transactions
on Geoscience and Remote Sensing: 30, 412-415.
Dobson, M. C., Ulaby, F. T., Pierce, L. E., Sharick, T. L., Bergen, K. M., kellndorfer, J.,
Kendra, J. R., Li, E., Lin, Y. C., Nashashibi, A., Sarabandi, K. and Siqueira, P.
(1995). Estimation of forest biophysical characteristics in Northern Michigan with
SIR-C/X-SAR. IEEE Transactions on Geoscience and Remote Sensing: 33, 877895.
Frazer, G. W., Canham, C. D. and Lertzman, K. P. (1999). Gap Light Analyzer (GLA),
Version 2.0: Imaging software to extract canopy structure and gap light
transmission indices from true-color fisheye photographs, users manual and
program documentation. Copyright © 1999: Simon Fraser University, Burnaby,
British Columbia, Canada, and the Institute of Ecosystem Studies, Millbrook,
New York, USA.
Hashim M. and Kadir, W. H. W. (1999). Comparison of JERS-1 and Radarsat synthetic
radar data for mapping mangrove and its biomass. In the 20* Asian Conference
on Remote Sensing, Nov. 22-25, 1999, Hong Kong, China. Poster session 1,1-5.
Henderson, F. M. and Lewis, A. J. (1998). Principles and applications of imaging
RADAR, Manual of remote sensing, vol.2, third edition. John Wiley &Sons, Inc.
Le Toan, T., Beaudoin, A. and Guyon, D. (1992). Relating forest biomass to SAR data.
IEEE Transactions on Geoscience and Remote Sensing: 30, 403-411.
Luckman A. J. (1997). The effects of topography on mechanisms of radar backscatter
from a coniferous forest and upland pasture. IEEE Transactions on Geoscience
and Remote Sensing: 36(5), 1830-1834.
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Luckman, A., Baker, J., Honzak, M. and Lucas, R. (1998). Tropical forest biomass
density estimation using JERS-1 SAR: seasonal variation, confidence limits, and
application to image mosaics. Remote Sensing of Environment: 63, 126-139.
Luckman, A., Baker, J., Kuplich, T. M., Yanasse, C. C. F. and Frery, A. C. (1997). A
Study o f the relationship between radar backscatter and regenerating tropical
forest biomass for spacebome SAR instruments. Remote Sensing o f Environment:
60, 1-13.
Melon, P., Martinez, J. M., Le Toan, T., Ulander, L. M. H. and Beaudoin, A. (2001). On
the retrieving o f forest stem volume from VHF SAR data: observation and
modeling. IEEE Transactions on Geoscience and Remote Sensing: 39(11), 23642372.
Peterson, D. L., Spanner, M. A., Running, S. W. and Teuber, K. B. (1987). Relationship
of thematic mapper simulator data to leaf area index of temperate coniferous
forest. Remote Sensing of Environment: 22, 323-341.
Pierce, L. E., Sarabandi, K. S. and Ulaby, F. T. (1994). Application o f an artificial neural
network in canopy scattering inversion. Intemational Journal of Remote Sensing:
15(16), 3263-3270.
Qi, J., Cabot, F., Moran, M. S. and Dedieu, G. (1995). Biophysical parameter estimations
using multidirectional spectral measurements. Remote Sensing o f Environment:
54,71-83.
Qi, J., Chehbouni, A., Huete, A. R. and Kerr, Y. (1994). A modified soil adjusted
vegetation index (MS AVI). Remote Sensing of Environment: 48, 119-126.
Qi, J., Kerr, Y. H., Moran, M. S., Weltz, M., Huete, A. R., Sorooshian, S. and Bryant, R.
(2000). Leaf area index estimates using remotely sensed data and BRDF models
in a semiarid region. Remote Sensing of Environment: 73: 18-30.
Ranson, K. J. and Sun G. (1997). An evaluation of AIRSAR and SIR-C/X-SAR images
for mapping northern forest attributes in Maine, USA. Remote Sensing of
Environment: 59, 203-222.
Saatchi, S. S. and McDonald, K. C. (1997). Coherent effects on microwave
backscattering models for forest canopies. IEEE Transactions on Geoscience and
Remote Sensing: 35, 1032-1044.
Santos, J. R., Freitas, C. C., Araujo, J. S., Dutra, L. V., Mura, J. C., Gama, F. F., Soler, L.
S., and Sant’Anna S. J. S. (2003). Airbome P-band SAR applied to the
aboveground biomass studies in Brazilian Tropical Rainforest. Remote Sensing of
Environment: 87, 482-493.
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Sun, G., Simonett, D. and Strahler, A. (1991). A radar backscatter model for
discontinuous coniferous forests. IEEE Transactions on Geoscience and Remote
Sensing: 29, 639-650.
Thailand Land Use and Land Cover Change Case Study (1997). Southeast Asia, IHDP,
IGBP, WCRP program.
Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht, K. S. and Briggs, J. M. (1999).
Relationships between leaf area index and Landsat TM spectral vegetation indices
across three temperate zone sites. Remote Sensing of Environment: 70, 52-68.
Ulaby, F.T., Sarabandi, K., Mcdonald, K., Whitt, M. and Dobson, M. C. (1990).
Michigan microwave canopy scattering model. Intemational Journal of Remote
Sensing: 11(2), 1223-1253.
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Table 6-1
tests.
Coefficients of cr° -biomass logarithmic curve fitting and their statistic
0
mod el
0
^SA R
0
m o d e l(n o -le a f)
mO
-14.22
-14.43
-19.09
ml
1.04
0.93
1.73
m2
-3.29
-11.10
-7.14
24.22
27.58
46.08
Correlation coefficient (R)
0.75
0.79
0.79
Equation
(j° = mO + m l* ln(biomass + m2)
164
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■7
8
X
X
■9
E
D)
‘co -10
</)
y = m1 + m2 * ln(M0+m3)
11
cPO
-12
Value
Error
m1
-13.838
1.3093
m2
0.96271
0.25785
m3
-5.4873
10.073
29.153
NA
0.75313
NA
Chisq
-13
0
100
200
300
400
500
600
b io m a ss
Figure 6-1
Scatterplot and curve fitting of <
j ]ers-\ ~biomass at all study sites
■m
Figure 6-2
Biomass
Biomass distribution estimated with a simple c7]ers-\ ~biomass regression
model.
165
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Figure 6-3
The LAI distribution derived from JERS-1 VNIR data in the study area.
166
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-/ -8
X
X
E
♦
♦
♦
♦ ♦
♦
-9 - ♦♦
O)
'55 -10 - V .
■o
o>
■o
V
-11 - ♦
♦
-12 ♦
-13 -14 0
200
600
400
biomass
(a)
0
X
-10
m
«>^x
4
| a
X
CD
^
-20
o
E
O)
'55 -30
T3
O
O
■o -40
O
o le a f
a
E
A le a f -s o il
+
+
•
X
• ^
-50
X branch
X b r a n c h -s o il
••
• tru n k
•
•
-t-tru n k -so il
-60
0
200
400
600
biomass
(b)
Figure 6-4
Relationships between ground-measured biomass and modeled
backscattering coefficients of forests (a) and tbeir components (b).
167
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-7
-8
m
2 - -9
c
o -10
/
St
CD
-11 / / /
O
O
C3)
.c
CD
-12
-13
O
(/) -14
O
CD
JD -15
-16
Figure 6-5
JERS-1 BAR
Model
Model (no-leaf)
100
200
300
400
biomass (ton/ha)
500
600
Logarithmic curve fitting of SAR observed and modeled backscattering
coefficients with biomass.
A
(a)
Figure 6-6
(b)
Leaf scattering (a) and its attenuation to the woody forests (b).
168
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i)
-■ m M m
w ,#
•3^-.
'■
Figure 6-7
Woody scattering in the study area.
Biomass
>3()i)
Figure 6-8
Biomass distribution with woody forest scattering in a regression model.
169
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w.
Figure 6-9
DBH (cm)
Modeled DBH distribution in the watershed.
B io m a s s
Figure 6-10
Forest biomass distribution from model inversion in the watershed.
170
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600
R M S E = 1 2 1 to n /h a (with outlier)
R M S E = 8 8 to n /h a (w ithout outlier)
:= 500
S2-14
400
300
200
100
0
100
200
300
400
500
600
measured biomass (ton/ha)
Figure 6-11
Scatterplot of modeled and measured biomass at study sites.
modeled
measured
standard error
mixed pine
trans
Figure 6-12
dry moist
ever ever
Average values of measured and modeled biomass (without outlier) and
standard error of the modeled biomass in each forest type.
171
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Chapter 7
Conclusions and Future Envisions
7.1
Concluding Remarks
The objective of this study is to quantitatively estimate biophysical attributes using both
optical and microwave (SAR) remote sensing techniques in tropical forests. Several
physical, semi-empirical, and empirical models were built to address this objective. The
study area is tropical environment in Mae Chaem Watershed, ChiangMai, Thailand. The
field measurements were described in Chapter 2. The biophysical attributes estimated in
this study include forest fractional cover (Chapter 3), leaf area index (Chapter 4), tree
height and stand density (Chapter 5), and aboveground biomass (Chapter 6).
A linear unmixing model was built to estimate forest fractional cover (Chapter 3). Instead
of using the spectral reflectance in the linear unmixing models that were commonly
applied in the past studies, a vegetation index - MSA VI, which is most linearly related to
the green leaf abundance in tropical forests when L A Iis less than 4.0 - was applied in this
study. Only two components were assumed in each pixel of the remote sensing imagery;
tree canopy and open area. The forest fractional cover thus represents the coverage of the
forest canopy in each pixel. A forest fractional cover map was built with a Landsat ETM+
image acquired in the dry season in the study area. It was validated with both groundmeasured fractional cover at 32 study sites (R^=0.76) and high-resolution (1 meter)
IKONOS calculated fractional cover data at 400 randomly selected locations (R^=0.70).
The fractional cover map was also adjusted to the wet season in which all forest types
172
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were flourishing and the seasonal variation of deciduous species in the forests was
reduced.
Leaf area index is highly correlated to forest fractional cover. A modified Gaussian
regression model was built to estimate leaf area index with forest fractional cover
(Chapter 4). Since it is impossible to measure leaf area index with LAI-2000 at the study
sites in the watershed, additional ground data were acquired in northern Michigan where
several forest areas were measured at different seasons to represent the forest conditions
in the study area in Thailand. Both LAI-2000 and fisheye photos were taken to measure
leaf area index and forest fractional cover in these forests. The regression model was
examined with a
goodness-of-fit test and then applied to the study area in Thailand.
The leaf area index in both dry and wet seasons were mapped in the study area. A narrow
zone, possibly the pine transition zone between mixed deciduous and evergreen forests
was shown in the leaf area map in the dry season. The leaf area index in the wet season
was much higher in the study area. But some forests not far from the large open areas
(villages, agricultural areas) at lower elevation have very low values even in the wet
season, indicating intense human disturbances in these forests.
To further retrieve forest structural information and aboveground biomass, a
micro wave/optical synergistic radiative transfer model was built in Chapter 5. The
volume scattering from woody components (branches, trunks) and their interaction with
ground surface were simulated while the leaf scattering was quantified with leaf area
index from JERS-1 VNIR optical data. The root-mean-square error (RMSE) of the
173
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model was 0.94 dB compared with the JERS-1 SAR backscattering coefficients. Forest
structural parameters, such as tree height and stand density, were estimated by model
inversion with least-square-error optimization techniques. The error of model inversion in
most of the study areas was less than 1 dB. In areas with high relief and steep slopes, the
topographic effects on JERS-1 SAR backscattering coefficients cannot be corrected and
the error o f model inversion could be higher than 4 dB, introducing high error to the
forest structural estimation of tree height and stand density. The RMSE of tree height
estimation was 3.8 meter and that of stand density estimation was 299 trees/ha. The tree
height in young secondary forests, mostly dry dipterocarp forests, was overestimated
while the estimation in other forests fitted well with ground measurements. The stand
density estimation in tropical evergreen forests did not work. It saturated at around 500
trees/ha. In accordance with the ground measurements, the modeled tree height was
negatively correlated with the modeled stand density.
Several methods o f biomass estimation with JERS-I SAR and VNIR data were examined
in Chapter 6. A simple regression model was firstly built to estimate biomass from JERSI SAR backscattering coefficients. The model was examined with a
test. It revealed a
trend of increasing biomass distribution from east to west of the study area that was
possibly the mis-calibration in the SAR data when converting slant-range to ground
pixels. The estimated biomass of tropical evergreen forests was very low and varied
greatly. With JERS-1 VNIR data, the leaf scattering and its attenuation to the woody
forests (branches and trunks) were quantified. Then the woody forest scattering after the
compensation o f leaf attenuation was modeled. A new regression model was built to
174
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estimate biomass with woody scattering in the study area. The woody forest scattering
was more sensitive to the biomass, but the topographic effect to the scattering also
became higher when the leaf attenuation was removed. The modeled biomass in tropical
evergreen forests was even lower with higher heterogeneity. None of the two methods
above could be validated in lack of additional ground measurements. The third method of
biomass estimation was allometric equations. With the microwave/optical synergistic
model and JERS-1 SAR and VNIR data, the tree height and stand density were estimated
in Chapter 5. The DBH was estimated from its linear relation to tree height. Therefore,
the biomass was able to be calculated with allometric equations. The biomass in this
method was overestimated in dry dipterocarp forests, but fitted well with ground
measurements in mixed deciduous forests. The biomass estimation in tropical evergreen
forests is highly heterogeneous because of the topographic effect and the mis-estimation
of stand density in Chapter 5. The root-mean-square error of the biomass estimation in
the study sites was 88 ton/ha.
In a summery, with both optical and microwave remote sensing imagery and radiative
transfer model, the forest fractional cover, leaf area index, tree height and stand density,
and aboveground biomass were mapped in the study area. Aside from forest fractional
cover, each other biophysical attribute is mainly (such as leaf area index) or partially
(such as tree height and stand density, and aboveground biomass) from the results in
earlier chapters. The error propagation between these attributes will be studied in the near
future.
175
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7.2
Challenges
The estimation o f biophysical attributes in tropical forests mountainous areas was very
challenging. The ground measurements are time and labor consuming and are often
limited because of the physical difficulty of access to the study sites. Moreover, the
ground measurements are not actual ground “truth” in many situations. For example, the
ground-measured forest fractional cover and leaf area index with fisheye photo and LAI2000 are actually projected cover and foliage area index. They are the combined
contribution from both green leaves and branches, trunks and any other non-open
components. On the other hand, the fractional cover and leaf area index estimation with
remotely sensed data are based on the spectral responses that are mostly sensitive to
green leaves. The validation of remote sensing estimated fractional cover and leaf area
index with ground measurements is thus biased.
Another challenge of ground measurements is to measure the tree structural parameters
such as tree height and stand density. The fixed-radius plot method often provides the
accurate measure of these parameters on the study area, but is too time- and labor­
consuming and not realistic in forests. The point-quadrant plot method is a more efficient
way of ground measurements, but often results in high bias to the ground data. In the late
succession of forests, there are usually many young trees that surroimd the old ones and,
therefore, there is high possibility of picking up young trees in each quadrant. The
measured tree height and stand density are thus underestimated. Assuming these data as
ground “truth”, the estimated biophysical parameters from remotely sensed data are often
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overestimated. In the future studies, the ground measurements with both methods at same
study sites could be compared and a possible compensation model could be developed.
Topographic effect in SAR images was the largest obstacle in microwave remote sensing.
The quality of topographic correction to the SAR images depends on both the system
characteristics of SAR sensor, the DEM data, and the target characteristics. In
mountainous areas with high relief and steep slopes, the topographic effects cannot be
corrected at all. The mis-correction results in high errors to the estimation of forest
biophysical attributes.
There are thousands of computing iterations in the model inversion and the canopy
scattering model is called at each iteration for each pixel of the SAR and optical images.
Therefore, the computation time is also very challenging in the estimation of forest
structural parameters. A possible solution is to do the model inversion and to estimate
biophysical attributes only at core locations in the study area. The attributes at other
pixels can be estimated with linear or polynomial interpolation.
When SAR and optical remote sensing data are applied synergistically, the acquisition of
SAR and optical data at the same time becomes critical. Although quite a few optical
sensors are available by now, there are only ERS-2, Radarsat, and Envisat together with
some Airborne sensors (for example, AIRSAR) still operative. SAR sensors in both ERS2 and Radarsat are in C-band and their application in tropical forests is thus very limited.
Fortunately, with high temporal resolution of optical data in the past years, it is possible
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to develop leaf area index maps on an annual base, which could provide important
information for the microwave/optical synergistic model.
In the near future, the only opportunity to acquire SAR/optical imagery at the same time
is the Advanced Land Observing Satellite (ALOS) designed by NASDA in Japan. It will
be launched in 2004 boarding one radar sensor and two optical sensors: Phrase Array
type L-band Synthetic Aperture Radar (PALSAR, a follow-up of JERS-1 SAR),
Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2, a follow-up of JERS1 VNIR), and Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM).
While PALSAR and AVNIR-2 acquire SAR and optical data almost simultaneously, the
PRISM sensor collects high-resolution elevation data that could improve the topographic
correction on both SAR and optical data. If launched successfully, ALOS data could
provide valuable information in studies of forests of mountainous areas.
7.3
Potential Applications for Other Studies
The results in this study could provide some quantitative information for the studies of
land use / land cover change and human-environment interactions. The variation of the
biophysical attributes distribution in the study area is related to the extent of human
disturbance and forest recovery, together with the difference in forest types, soil texture,
and topography. For example, the biophysical parameters had a pattern of rapid increase
from downhill to uphill in Mae Chaem Watershed. This phenomenon revealed the effects
of human activities on land use and land cover dynamics. The forests at downhill suffer
from intense human disturbance of burning for agriculture and cutting for firewood.
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Therefore, the values of the biophysical attributes are very low. At higher elevation, due
to the geographical difficulty, clear-outs for small area agriculture fields and selective
logging for valuable trees become the major human activities. Consequently, the forest
biophysical attributes have higher values. On the top of the mountain, the forests are only
accessible to highland tribes whose activities are very limited and therefore, the
biophysical distribution of these forests varies little.
When applied to a larger scale, this research could also serve as a guide for ecosystem
assessment and further decision-making for environment protection.
For a long time, the binary forest/non-forest mapping has been applied in the assessment
of carbon cycle and global climate change studies. However, different forests, such as
mature forests, selectively logged forests, highly degraded and fragmented forests, and
secondly regrowed forests which have different fractional cover and biomass, play
different roles in carbon sequestration. The methods developed in this study can provide
quantitative estimation of fractional cover and biomass in the forests that are important
input parameters in carbon and climate models. With this information, we can assess the
net carbon flux in forest ecosystems at higher accuracy.
For future studies, the methods developed in this study could be applied to estimate
biophysical attributes in other vegetated ecosystems like temperate forests, agricultural
cropping systems, and rangelands. In each ecosystem, only the probability distribution
functions of the vegetation components and their geophysical and biophysical parameters
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in the synergistic radiative transfer model need to be re-evaluated. The model could also
be modified and applied to SAR data at different polarizations and radar frequencies in
the estimate of biophysical attributes in different ecosystems.
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